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Rokhshad R, Zhang P, Mohammad-Rahimi H, Pitchika V, Entezari N, Schwendicke F. Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study. J Dent 2024; 144:104938. [PMID: 38499280 DOI: 10.1016/j.jdent.2024.104938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 03/06/2024] [Accepted: 03/11/2024] [Indexed: 03/20/2024] Open
Abstract
OBJECTIVES Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry. METHODS Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n = 20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency. RESULTS Pediatric dentists were significantly more accurate (mean±SD 96.67 %± 4.3 %) than other clinicians and chatbots (p < 0.001). General dentists (88.0 % ± 6.1 %) also demonstrated significantly higher accuracy than chatbots (p < 0.001), followed by students (80.8 %±6.9 %). ChatGPT showed the highest accuracy (78 %±3 %) among chatbots. All chatbots except ChatGPT3.5 showed acceptable consistency (Cronbach alpha>0.7). CLINICAL SIGNIFICANCE Based on this pilot study, chatbots may be valuable adjuncts for educational purposes and for distributing information to patients. However, they are not yet ready to serve as substitutes for human clinicians in diagnostic decision-making. CONCLUSION In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.
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Affiliation(s)
- Rata Rokhshad
- Department of Pediatric Dentistry, University of Alabama at Birmingham, Birmingham, AL, USA.
| | - Ping Zhang
- Department of Pediatric Dentistry, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Vinay Pitchika
- Department of Conservative Dentistry and Periodontology, LMU Klinikum Munich, Germany
| | - Niloufar Entezari
- Department of pediatric dentistry, School of Dentistry, Qom University of Medical Sciences, Qom, Iran
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Conservative Dentistry and Periodontology, LMU Klinikum Munich, Germany
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Tinanoff N, Banerjee A, Buzalaf MAR, Chen JW, Dhar V, Ekstrand KR, Fontana M, Innes N, Koo H, Listl S, Lo ECM, Potgieter N, Schwendicke F, Sharkov N, Twetman S, Vargas K. Principles and care pathways for caries management in children: IAPD Rome forum. Int J Paediatr Dent 2024. [PMID: 38654429 DOI: 10.1111/ipd.13192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 03/29/2024] [Accepted: 04/04/2024] [Indexed: 04/26/2024]
Affiliation(s)
- Norman Tinanoff
- Department of Orthodontics and Pediatric Dentistry, University of Maryland, School of Dentistry, Baltimore, Maryland, USA
| | - Avijit Banerjee
- Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | | | - Jung-Wei Chen
- Department of Pediatric Dentistry, Loma Linda University School of Dentistry, Loma Linda, California, USA
| | - Vineets Dhar
- Department of Orthodontics and Pediatric Dentistry, University of Maryland, School of Dentistry, Baltimore, Maryland, USA
| | - Kim R Ekstrand
- Department of Odontology, University of Copenhagen, Copenhagen, Denmark
| | - Margherita Fontana
- Department of Cariology, Restorative Sciences & Endodontics, University of Michigan, Ann Arbor, Michigan, USA
| | - Nicola Innes
- School of Dentistry, Cardiff Dental School, Cardiff University, Cardiff, UK
| | - Hyun Koo
- Department of Orthodontics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Stefan Listl
- Department of Dentistry-Quality and Safety of Oral Health, Radboud University Medical Center, Radboud Institute for Health Sciences, Nijmegen, The Netherlands
| | | | - Nicoline Potgieter
- Department of Paediatric Dentistry, University of the Western Cape, Mitchells Plain, South Africa
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research Charité-Universitätsmedizin Berlin, Universitatsmedizin, Berlin, Germany
| | - Nikolai Sharkov
- Department of Paediatric Dentistry, Faculty of Dental Medicine, Medical University, Sofia, Bulgaria
| | - Svante Twetman
- Department of Odontology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kaaren Vargas
- Private Practice, Corridor Kids Pediatric Dentistry, North Liberty, North Liberty, Iowa, USA
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Ferrari AJ, Santomauro DF, Aali A, Abate YH, Abbafati C, Abbastabar H, Abd ElHafeez S, Abdelmasseh M, Abd-Elsalam S, Abdollahi A, Abdullahi A, Abegaz KH, Abeldaño Zuñiga RA, Aboagye RG, Abolhassani H, Abreu LG, Abualruz H, Abu-Gharbieh E, Abu-Rmeileh NME, Ackerman IN, Addo IY, Addolorato G, Adebiyi AO, Adepoju AV, Adewuyi HO, Afyouni S, Afzal S, Afzal S, Agodi A, Ahmad A, Ahmad D, Ahmad F, Ahmad S, Ahmed A, Ahmed LA, Ahmed MB, Ajami M, Akinosoglou K, Akkaif MA, Al Hasan SM, Alalalmeh SO, Al-Aly Z, Albashtawy M, Aldridge RW, Alemu MD, Alemu YM, Alene KA, Al-Gheethi AAS, Alharrasi M, Alhassan RK, Ali MU, Ali R, Ali SSS, Alif SM, Aljunid SM, Al-Marwani S, Almazan JU, Alomari MA, Al-Omari B, Altaany Z, Alvis-Guzman N, Alvis-Zakzuk NJ, Alwafi H, Al-Wardat MS, Al-Worafi YM, Aly S, Alzoubi KH, Amare AT, Amegbor PM, Ameyaw EK, Amin TT, Amindarolzarbi A, Amiri S, Amugsi DA, Ancuceanu R, Anderlini D, Anderson DB, Andrade PP, Andrei CL, Ansari H, Antony CM, Anwar S, Anwar SL, Anwer R, Anyanwu PE, Arab JP, Arabloo J, Arafat M, Araki DT, Aravkin AY, Arkew M, Armocida B, Arndt MB, Arooj M, Artamonov AA, Aruleba RT, Arumugam A, Ashbaugh C, Ashemo MY, Ashraf M, Asika MO, Askari E, Astell-Burt T, Athari SS, Atorkey P, Atout MMW, Atreya A, Aujayeb A, Ausloos M, Avan A, Awotidebe AW, Awuviry-Newton K, Ayala Quintanilla BP, Ayuso-Mateos JL, Azadnajafabad S, Azevedo RMS, Babu AS, Badar M, Badiye AD, Baghdadi S, Bagheri N, Bah S, Bai R, Baker JL, Bakkannavar SM, Bako AT, Balakrishnan S, Bam K, Banik PC, Barchitta M, Bardhan M, Bardideh E, Barker-Collo SL, Barqawi HJ, Barrow A, Barteit S, Barua L, Bashiri Aliabadi S, Basiru A, Basu S, Basu S, Bathini PP, Batra K, Baune BT, Bayileyegn NS, Behnam B, Behnoush AH, Beiranvand M, Bejarano Ramirez DF, Bell ML, Bello OO, Beloukas A, Bensenor IM, Berezvai Z, Bernabe E, Bernstein RS, Bettencourt PJG, Bhagavathula AS, Bhala N, Bhandari D, Bhargava A, Bhaskar S, Bhat V, Bhatti GK, Bhatti JS, Bhatti MS, Bhatti R, Bhutta ZA, Bikbov B, Bishai JD, Bisignano C, Bitra VR, Bjørge T, Bodolica V, Bodunrin AO, Bogale EK, Bonakdar Hashemi M, Bonny A, Bora Basara B, Borhany H, Boxe C, Brady OJ, Bragazzi NL, Braithwaite D, Brant LC, Brauer M, Breitner S, Brenner H, Brown J, Brugha T, Bulamu NB, Buonsenso D, Burkart K, Burns RA, Busse R, Bustanji Y, Butt ZA, Byun J, Caetano dos Santos FL, Calina D, Cámera LA, Campos-Nonato IR, Cao C, Capodici A, Carr S, Carreras G, Carugno A, Carvalho M, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Catapano AL, Cattaruzza MS, Caye A, Cegolon L, Cembranel F, Cenderadewi M, Cerin E, Chakraborty PA, Chan JSK, Chan RNC, Chandika RM, Chandrasekar EK, Charalampous P, Chattu VK, Chatzimavridou-Grigoriadou V, Chen AW, Chen AT, Chen CS, Chen H, Chen NM, Cheng ETW, Chimed-Ochir O, Chimoriya R, Ching PR, Cho WCS, Choi S, Chong B, Chong YY, Choudhari SG, Chowdhury R, Christensen SWM, Chu DT, Chukwu IS, Chung E, Chung E, Chutiyami M, Claassens MM, Cogen RM, Columbus A, Conde J, Cortesi PA, Cousin E, Criqui MH, Cruz-Martins N, Dadras O, Dai S, Dai X, Dai Z, Dalaba MA, Damiani G, Das JK, Das S, Dashti M, Dávila-Cervantes CA, Davletov K, De Leo D, Debele AT, Debopadhaya S, DeCleene NK, Deeba F, Degenhardt L, Del Bo' C, Delgado-Enciso I, Demetriades AK, Denova-Gutiérrez E, Dervenis N, Desai HD, Desai R, Deuba K, Dhama K, Dharmaratne SD, Dhingra S, Dias da Silva D, Diaz D, Diaz LA, Diaz MJ, Dima A, Ding DD, Dirac MA, Do THP, do Prado CB, Dohare S, Dominguez RMV, Dong W, Dongarwar D, D'Oria M, Dorsey ER, Doshmangir L, Dowou RK, Driscoll TR, Dsouza HL, Dsouza V, Dube J, Dumith SC, Duncan BB, Duraes AR, Duraisamy S, Durojaiye OC, Dzianach PA, Dziedzic AM, Eboreime E, Ebrahimi A, Edinur HA, Edvardsson D, Eikemo TA, Eini E, Ekholuenetale M, Ekundayo TC, El Sayed I, El Tantawi M, Elbarazi I, Elemam NM, ElGohary GMT, Elhadi M, Elmeligy OAA, ELNahas G, Elshaer M, Elsohaby I, Engelbert Bain L, Erkhembayar R, Eshrati B, Estep K, Fabin N, Fagbamigbe AF, Falzone L, Fareed M, Farinha CSES, Faris MEM, Faro A, Farrokhi P, Fatehizadeh A, Fauk NK, Feigin VL, Feng X, Fereshtehnejad SM, Feroze AH, Ferreira N, Ferreira PH, Fischer F, Flavel J, Flood D, Flor LS, Foigt NA, Folayan MO, Force LM, Fortuna D, Foschi M, Franklin RC, Freitas A, Fukumoto T, Furtado JM, Gaal PA, Gadanya MA, Gaidhane AM, Gaihre S, Galali Y, Ganbat M, Gandhi AP, Ganesan B, Ganie MA, Ganiyani MA, Gardner WM, Gebi TG, Gebregergis MW, Gebrehiwot M, Gebremariam TBB, Gebremeskel TG, Gela YY, Georgescu SR, Getachew Obsa A, Gething PW, Getie M, Ghadiri K, Ghadirian F, Ghailan KY, Ghajar A, Ghasemi M, Ghasempour Dabaghi G, Ghasemzadeh A, Ghazy RM, Gholamrezanezhad A, Ghorbani M, Ghotbi E, Gibson RM, Gill TK, Ginindza TG, Girmay A, Glasbey JC, Göbölös L, Godinho MA, Goharinezhad S, Goldust M, Golechha M, Goleij P, Gona PN, Gorini G, Goulart AC, Grada A, Grivna M, Guan SY, Guarducci G, Gubari MIM, Gudeta MD, Guha A, Guicciardi S, Gulati S, Gulisashvili D, Gunawardane DA, Guo C, Gupta AK, Gupta B, Gupta I, Gupta M, Gupta R, Gupta VB, Gupta VK, Gupta VK, Gutiérrez RA, Habibzadeh F, Habibzadeh P, Haddadi R, Hadi NR, Haep N, Hafezi-Nejad N, Hafiz A, Hagins H, Halboub ES, Halimi A, Haller S, Halwani R, Hamilton EB, Hankey GJ, Hannan MA, Haque MN, Harapan H, Haro JM, Hartvigsen J, Hasaballah AI, Hasan I, Hasanian M, Hasnain MS, Hassan A, Haubold J, Havmoeller RJ, Hay SI, Hayat K, Hebert JJ, Hegazi OE, Heidari G, Helfer B, Hemmati M, Hendrie D, Henson CA, Hezam K, Hiraike Y, Hoan NQ, Holla R, Hon J, Hossain MM, Hosseinzadeh H, Hosseinzadeh M, Hostiuc M, Hostiuc S, Hsu JM, Huang J, Hugo FN, Hushmandi K, Hussain J, Hussein NR, Huynh CK, Huynh HH, Hwang BF, Iannucci VC, Ihler AL, Ikiroma AI, Ikuta KS, Ilesanmi OS, Ilic IM, Ilic MD, Imam MT, Immurana M, Irham LM, Islam MR, Islam SMS, Islami F, Ismail F, Ismail NE, Isola G, Iwagami M, Iwu CCD, Iyer M, Jaafari J, Jacobsen KH, Jadidi-Niaragh F, Jafarinia M, Jaggi K, Jahankhani K, Jahanmehr N, Jahrami H, Jain A, Jain N, Jairoun AA, Jaiswal A, Jakovljevic M, Jatau AI, Javadov S, Javaheri T, Jayapal SK, Jayaram S, Jee SH, Jeganathan J, Jeyakumar A, Jha AK, Jiang H, Jin Y, Jonas JB, Joo T, Joseph A, Joseph N, Joshua CE, Jozwiak JJ, Jürisson M, K V, Kaambwa B, Kabir A, Kabir Z, Kadashetti V, Kalani R, Kalankesh LR, Kaliyadan F, Kalra S, Kamenov K, Kamyari N, Kanagasabai T, Kandel H, Kanmanthareddy AR, Kanmodi KK, Kantar RS, Karaye IM, Karim A, Karimi SE, Karimi Y, Kasraei H, Kassel MB, Kauppila JH, Kawakami N, Kayode GA, Kazemi F, Kazemian S, Keikavoosi-Arani L, Keller C, Kempen JH, Kerr JA, Keshtkar K, Kesse-Guyot E, Keykhaei M, Khajuria H, Khalaji A, Khalid A, Khalid N, Khalilian A, Khamesipour F, Khan A, Khan I, Khan M, Khan MAB, Khanmohammadi S, Khatab K, Khatami F, Khatatbeh MM, Khater AM, Khayat Kashani HR, Khidri FF, Khodadoust E, Khormali M, Khorrami Z, Kifle ZD, Kim MS, Kimokoti RW, Kisa A, Kisa S, Knudsen AKS, Kocarnik JM, Kochhar S, Koh HY, Kolahi AA, Kompani F, Koren G, Korzh O, Kosen S, Koulmane Laxminarayana SL, Krishan K, Krishna V, Krishnamoorthy V, Kuate Defo B, Kuddus MA, Kuddus M, Kuitunen I, Kulkarni V, Kumar M, Kumar N, Kumar R, Kurmi OP, Kusuma D, Kyu HH, La Vecchia C, Lacey B, Ladan MA, Laflamme L, Lafranconi A, Lahariya C, Lai DTC, Lal DK, Lalloo R, Lallukka T, Lám J, Lan Q, Lan T, Landires I, Lanfranchi F, Langguth B, Laplante-Lévesque A, Larijani B, Larsson AO, Lasrado S, Lauriola P, Le HH, Le LKD, Le NHH, Le TDT, Leasher JL, Ledda C, Lee M, Lee PH, Lee SW, Lee SW, Lee WC, Lee YH, LeGrand KE, Lenzi J, Leong E, Leung J, Li MC, Li W, Li X, Li Y, Li Y, Lim LL, Lim SS, Lindstrom M, Linn S, Liu G, Liu R, Liu S, Liu W, Liu X, Liu X, Llanaj E, Lo CH, López-Bueno R, Loreche AM, Lorenzovici L, Lozano R, Lubinda J, Lucchetti G, Lunevicius R, Lusk JB, lv H, Ma ZF, Machairas N, Madureira-Carvalho ÁM, Magaña Gómez JA, Maghazachi AA, Maharjan P, Mahasha PW, Maheri M, Mahjoub S, Mahmoud MA, Mahmoudi E, Majeed A, Makris KC, Malakan Rad E, Malhotra K, Malik AA, Malik I, Malta DC, Manla Y, Mansour A, Mansouri P, Mansournia MA, Mantilla Herrera AM, Mantovani LG, Manu E, Marateb HR, Mardi P, Martinez G, Martinez-Piedra R, Martini D, Martins-Melo FR, Martorell M, Marx W, Maryam S, Marzo RR, Mathangasinghe Y, Mathieson S, Mathioudakis AG, Mattumpuram J, Maugeri A, Mayeli M, Mazidi M, Mazzotti A, McGrath JJ, McKee M, McKowen ALW, McPhail MA, Mehrabani-Zeinabad K, Mehrabi Nasab E, Mekene Meto T, Mendoza W, Menezes RG, Mensah GA, Mentis AFA, Meo SA, Meresa HA, Meretoja A, Meretoja TJ, Mersha AM, Mestrovic T, Mettananda KCD, Mettananda S, Michalek IM, Miller PA, Miller TR, Mills EJ, Minh LHN, Mirijello A, Mirrakhimov EM, Mirutse MK, Mirza-Aghazadeh-Attari M, Mirzaei M, Mirzaei R, Misganaw A, Mishra AK, Mitchell PB, Mittal C, Moazen B, Moberg ME, Mohamed J, Mohamed MFH, Mohamed NS, Mohammadi E, Mohammadi S, Mohammed H, Mohammed S, Mohammed S, Mohr RM, Mokdad AH, Molinaro S, Momtazmanesh S, Monasta L, Mondello S, Moodi Ghalibaf A, Moradi M, Moradi Y, Moradi-Lakeh M, Moraga P, Morawska L, Moreira RS, Morovatdar N, Morrison SD, Morze J, Mosapour A, Mosser JF, Mossialos E, Motappa R, Mougin V, Mouodi S, Mrejen M, Msherghi A, Mubarik S, Mueller UO, Mulita F, Munjal K, Murillo-Zamora E, Murlimanju BV, Mustafa G, Muthu S, Muzaffar M, Myung W, Nagarajan AJ, Naghavi P, Naik GR, Nainu F, Nair S, Najmuldeen HHR, Nangia V, Naqvi AA, Narayana AI, Nargus S, Nascimento GG, Nashwan AJ, Nasrollahizadeh A, Nasrollahizadeh A, Natto ZS, Nayak BP, Nayak VC, Nduaguba SO, Negash H, Negoi I, Negoi RI, Nejadghaderi SA, Nesbit OD, Netsere HB, Ng M, Nguefack-Tsague G, Ngunjiri JW, Nguyen DH, Nguyen HQ, Niazi RK, Nikolouzakis TK, Nikoobar A, Nikoomanesh F, Nikpoor AR, Nnaji CA, Nnyanzi LA, Noman EA, Nomura S, Norrving B, Nri-Ezedi CA, Ntaios G, Ntsekhe M, Nurrika D, Nzoputam CI, Nzoputam OJ, Oancea B, Odetokun IA, O'Donnell MJ, Oguntade AS, Oguta JO, Okati-Aliabad H, Okeke SR, Okekunle AP, Okonji OC, Olagunju AT, Olasupo OO, Olatubi MI, Oliveira GMM, Olufadewa II, Olusanya BO, Olusanya JO, Omar HA, Omer GL, Omonisi AEE, Onie S, Onwujekwe OE, Ordak M, Orish VN, Ortega-Altamirano DV, Ortiz A, Ortiz-Brizuela E, Osman WMS, Ostroff SM, Osuagwu UL, Otoiu A, Otstavnov N, Otstavnov SS, Ouyahia A, Ouyang G, Owolabi MO, P A MP, Padron-Monedero A, Padubidri JR, Palicz T, Palladino C, Pan F, Pandi-Perumal SR, Pangaribuan HU, Panos GD, Panos LD, Pantea Stoian AM, Pardhan S, Parikh RR, Pashaei A, Pasovic M, Passera R, Patel J, Patel SK, Patil S, Patoulias D, Patthipati VS, Pawar S, Pazoki Toroudi H, Pease SA, Peden AE, Pedersini P, Peng M, Pensato U, Pepito VCF, Peprah EK, Peprah P, Perdigão J, Pereira MO, Perianayagam A, Perico N, Pesudovs K, Petermann-Rocha FE, Petri WA, Pham HT, Philip AK, Phillips MR, Pigeolet M, Pigott DM, Pillay JD, Piracha ZZ, Pirouzpanah S, Plass D, Plotnikov E, Poddighe D, Polinder S, Postma MJ, Pourtaheri N, Prada SI, Pradhan PMS, Prakash V, Prasad M, Prates EJS, Priscilla T, Pritchett N, Puri P, Puvvula J, Qasim NH, Qattea I, Qazi AS, Qian G, Rabiee Rad M, Radhakrishnan RA, Radhakrishnan V, Raeisi Shahraki H, Rafferty Q, Raggi A, Raghav PR, Rahim MJ, Rahman MM, Rahman MHU, Rahman M, Rahman MA, Rahmani S, Rahmanian M, Rahmawaty S, Rajaa S, Ramadan MM, Ramasamy SK, Ramasubramani P, Ramazanu S, Rana K, Ranabhat CL, Rancic N, Rane A, Rao CR, Rao K, Rao M, Rao SJ, Rashidi MM, Rathnaiah Babu G, Rauniyar SK, Rawaf DL, Rawaf S, Razo C, Reddy MMRK, Redwan EMM, Reifels L, Reiner Jr RC, Remuzzi G, Renzaho AMN, Reshmi B, Reyes LF, Rezaei N, Rezaei N, Rezaei N, Rezaei Hachesu P, Rezaeian M, Rickard J, Rodrigues CF, Rodriguez JAB, Roever L, Ronfani L, Roshandel G, Rotimi K, Rout HS, Roy B, Roy N, Roy P, Rubagotti E, S N C, Saad AMA, Saber-Ayad MM, Sabour S, Sacco S, Sachdev PS, Saddik B, Saddler A, Sadee BA, Sadeghi E, Sadeghi M, Saeb MR, Saeed U, Safi SZ, Sagar R, Sagoe D, Saif Z, Sajid MR, Sakshaug JW, Salam N, Salami AA, Salaroli LB, Saleh MA, Salem MR, Salem MZY, Sallam M, Samadzadeh S, Samargandy S, Samodra YL, Samy AM, Sanabria J, Sanna F, Santos IS, Santric-Milicevic MM, Sarasmita MA, Sarikhani Y, Sarmiento-Suárez R, Sarode GS, Sarode SC, Sarveazad A, Sathian B, Sathyanarayan A, Satpathy M, Sawhney M, Scarmeas N, Schaarschmidt BM, Schmidt MI, Schneider IJC, Schumacher AE, Schwebel DC, Schwendicke F, Sedighi M, Senapati S, Senthilkumaran S, Sepanlou SG, Sethi Y, Setoguchi S, Seylani A, Shadid J, Shafie M, Shah H, Shah NS, Shah PA, Shahbandi A, Shahid S, Shahid W, Shahwan MJ, Shaikh MA, Shakeri A, Shalash AS, Sham S, Shamim MA, Shamshirgaran MA, Shamsi MA, Shanawaz M, Shankar A, Shannawaz M, Sharath M, Sharifan A, Sharifi-Rad J, Sharma M, Sharma R, Sharma S, Sharma U, Sharma V, Shastry RP, Shavandi A, Shayan AM, Shayan M, Shehabeldine AME, Shetty PH, Shibuya K, Shifa JE, Shiferaw D, Shiferaw WS, Shigematsu M, Shiri R, Shitaye NA, Shittu A, Shivakumar KM, Shivarov V, Shokati Eshkiki Z, Shool S, Shrestha S, Shuval K, Sibhat MM, Siddig EE, Sigfusdottir ID, Silva DAS, Silva JP, Silva LMLR, Silva S, Simpson CR, Singal A, Singh A, Singh BB, Singh H, Singh JA, Singh M, Singh P, Skou ST, Sleet DA, Slepak ELN, Solanki R, Soliman SSM, Song S, Song Y, Sorensen RJD, Soriano JB, Soyiri IN, Spartalis M, Sreeramareddy CT, Stark BA, Starodubova AV, Stein C, Stein DJ, Steiner C, Steiner TJ, Steinmetz JD, Steiropoulos P, Stockfelt L, Stokes MA, Subedi NS, Subramaniyan V, Suemoto CK, Suleman M, Suliankatchi Abdulkader R, Sultana A, Sundström J, Swain CK, Szarpak L, Tabaee Damavandi P, Tabarés-Seisdedos R, Tabatabaei Malazy O, Tabatabaeizadeh SA, Tabatabai S, Tabche C, Tabish M, Tadakamadla SK, Taheri Abkenar Y, Taheri Soodejani M, Taherkhani A, Taiba J, Talaat IM, Talukder A, Tampa M, Tamuzi JL, Tan KK, Tandukar S, Tang H, Tavakoli Oliaee R, Tavangar SM, Teimoori M, Temsah MH, Teramoto M, Thangaraju P, Thankappan KR, Thapar R, Thayakaran R, Thirunavukkarasu S, Thomas N, Thomas NK, Thum CCC, Tichopad A, Ticoalu JHV, Tillawi T, Tiruye TY, Tobe-Gai R, Tonelli M, Topor-Madry R, Torre AE, Touvier M, Tovani-Palone MR, Tran JT, Tran MTN, Tran NM, Tran NH, Trico D, Tromans SJ, Truyen TTTT, Tsatsakis A, Tsegay GM, Tsermpini EE, Tumurkhuu M, Tyrovolas S, Udoh A, Umair M, Umakanthan S, Umar TP, Undurraga EA, Unim B, Unnikrishnan B, Unsworth CA, Upadhyay E, Urso D, Usman JS, Vahabi SM, Vaithinathan AG, Van den Eynde J, Varga O, Varma RP, Vart P, Vasankari TJ, Vasic M, Vaziri S, Vellingiri B, Venketasubramanian N, Veroux M, Verras GI, Vervoort D, Villafañe JH, Violante FS, Vlassov V, Vollset SE, Volovat SR, Vongpradith A, Waheed Y, Wang C, Wang F, Wang N, Wang S, Wang Y, Wang YP, Ward P, Wassie EG, Weaver MR, Weerakoon KG, Weintraub RG, Weiss DJ, Weldemariam AH, Wells KM, Wen YF, Whisnant JL, Whiteford HA, Wiangkham T, Wickramasinghe DP, Wickramasinghe ND, Wilandika A, Wilkerson C, Willeit P, Wimo A, Woldegebreal DH, Wolf AW, Wong YJ, Woolf AD, Wu C, Wu F, Wu X, Wu Z, Wulf Hanson S, Xia Y, Xiao H, Xu X, Xu YY, Yadav L, Yadollahpour A, Yaghoubi S, Yamagishi K, Yang L, Yano Y, Yao Y, Yaribeygi H, Yazdanpanah MH, Ye P, Yehualashet SS, Yesuf SA, Yezli S, Yiğit A, Yiğit V, Yigzaw ZA, Yismaw Y, Yon DK, Yonemoto N, Younis MZ, Yu C, Yu Y, Yusuf H, Zahid MH, Zakham F, Zaki L, Zaki N, Zaman BA, Zamora N, Zand R, Zandieh GGZ, Zar HJ, Zarrintan A, Zastrozhin MS, Zhang H, Zhang N, Zhang Y, Zhao H, Zhong C, Zhong P, Zhou J, Zhu Z, Ziafati M, Zielińska M, Zimsen SRM, Zoladl M, Zumla A, Zyoud SH, Vos T, Murray CJL. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00757-8. [PMID: 38642570 DOI: 10.1016/s0140-6736(24)00757-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/07/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. METHODS The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. FINDINGS Global DALYs increased from 2·63 billion (95% UI 2·44-2·85) in 2010 to 2·88 billion (2·64-3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7-17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8-6·3) in 2020 and 7·2% (4·7-10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0-234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7-198·3]), neonatal disorders (186·3 million [162·3-214·9]), and stroke (160·4 million [148·0-171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3-51·7) and for diarrhoeal diseases decreased by 47·0% (39·9-52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54-1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5-9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0-19·8]), depressive disorders (16·4% [11·9-21·3]), and diabetes (14·0% [10·0-17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7-27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6-63·6) in 2010 to 62·2 years (59·4-64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6-2·9) between 2019 and 2021. INTERPRETATION Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. FUNDING Bill & Melinda Gates Foundation.
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Uribe SE, Maldupa I, Kavadella A, El Tantawi M, Chaurasia A, Fontana M, Marino R, Innes N, Schwendicke F. Artificial intelligence chatbots and large language models in dental education: Worldwide survey of educators. Eur J Dent Educ 2024. [PMID: 38586899 DOI: 10.1111/eje.13009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/15/2024] [Accepted: 03/18/2024] [Indexed: 04/09/2024]
Abstract
INTRODUCTION Interest is growing in the potential of artificial intelligence (AI) chatbots and large language models like OpenAI's ChatGPT and Google's Gemini, particularly in dental education. To explore dental educators' perceptions of AI chatbots and large language models, specifically their potential benefits and challenges for dental education. MATERIALS AND METHODS A global cross-sectional survey was conducted in May-June 2023 using a 31-item online-questionnaire to assess dental educators' perceptions of AI chatbots like ChatGPT and their influence on dental education. Dental educators, representing diverse backgrounds, were asked about their use of AI, its perceived impact, barriers to using chatbots, and the future role of AI in this field. RESULTS 428 dental educators (survey views = 1516; response rate = 28%) with a median [25/75th percentiles] age of 45 [37, 56] and 16 [8, 25] years of experience participated, with the majority from the Americas (54%), followed by Europe (26%) and Asia (10%). Thirty-one percent of respondents already use AI tools, with 64% recognising their potential in dental education. Perception of AI's potential impact on dental education varied by region, with Africa (4[4-5]), Asia (4[4-5]), and the Americas (4[3-5]) perceiving more potential than Europe (3[3-4]). Educators stated that AI chatbots could enhance knowledge acquisition (74.3%), research (68.5%), and clinical decision-making (63.6%) but expressed concern about AI's potential to reduce human interaction (53.9%). Dental educators' chief concerns centred around the absence of clear guidelines and training for using AI chatbots. CONCLUSION A positive yet cautious view towards AI chatbot integration in dental curricula is prevalent, underscoring the need for clear implementation guidelines.
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Affiliation(s)
- Sergio E Uribe
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia
- Faculty of Dentistry, Universidad de Valparaiso, Valparaíso, Chile
- Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia
- ITU/WHO Focus Group AI on Health, Topic Group Dental, Geneva, Switzerland
| | - Ilze Maldupa
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia
| | - Argyro Kavadella
- School of Dentistry, European University Cyprus, Nicosia, Cyprus
| | - Maha El Tantawi
- Faculty of Dentistry, Alexandria University, Alexandria, Egypt
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group AI on Health, Topic Group Dental, Geneva, Switzerland
- Department of Oral Medicine & Radiology, King George's Medical University, Lucknow, Uttar Pradesh, India
| | - Margherita Fontana
- Department of Cariology, Restorative Sciences and Endodontics, School of Dentistry, University of Michigan, Ann Arbor, Michigan, USA
| | - Rodrigo Marino
- Melbourne Dental School, The University of Melbourne, Melbourne, Victoria, Australia
| | - Nicola Innes
- School of Dentistry, College of Biomedical & Life Sciences, Cardiff University, Cardiff, UK
| | - Falk Schwendicke
- ITU/WHO Focus Group AI on Health, Topic Group Dental, Geneva, Switzerland
- Department of Conservative Dentistry and Periodontology, Ludwig-Maximilians-University Munich, Munich, Germany
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Naghavi M, Ong KL, Aali A, Ababneh HS, Abate YH, Abbafati C, Abbasgholizadeh R, Abbasian M, Abbasi-Kangevari M, Abbastabar H, Abd ElHafeez S, Abdelmasseh M, Abd-Elsalam S, Abdelwahab A, Abdollahi M, Abdollahifar MA, Abdoun M, Abdulah DM, Abdullahi A, Abebe M, Abebe SS, Abedi A, Abegaz KH, Abhilash ES, Abidi H, Abiodun O, Aboagye RG, Abolhassani H, Abolmaali M, Abouzid M, Aboye GB, Abreu LG, Abrha WA, Abtahi D, Abu Rumeileh S, Abualruz H, Abubakar B, Abu-Gharbieh E, Abu-Rmeileh NME, Aburuz S, Abu-Zaid A, Accrombessi MMK, Adal TG, Adamu AA, Addo IY, Addolorato G, Adebiyi AO, Adekanmbi V, Adepoju AV, Adetunji CO, Adetunji JB, Adeyeoluwa TE, Adeyinka DA, Adeyomoye OI, Admass BAA, Adnani QES, Adra S, Afolabi AA, Afzal MS, Afzal S, Agampodi SB, Agasthi P, Aggarwal M, Aghamiri S, Agide FD, Agodi A, Agrawal A, Agyemang-Duah W, Ahinkorah BO, Ahmad A, Ahmad D, Ahmad F, Ahmad MM, Ahmad S, Ahmad S, Ahmad T, Ahmadi K, Ahmadzade AM, Ahmed A, Ahmed A, Ahmed H, Ahmed LA, Ahmed MS, Ahmed MS, Ahmed MB, Ahmed SA, Ajami M, Aji B, Akara EM, Akbarialiabad H, Akinosoglou K, Akinyemiju T, Akkaif MA, Akyirem S, Al Hamad H, Al Hasan SM, Alahdab F, Alalalmeh SO, Alalwan TA, Al-Aly Z, Alam K, Alam M, Alam N, Al-amer RM, Alanezi FM, Alanzi TM, Al-Azzam S, Albakri A, Albashtawy M, AlBataineh MT, Alcalde-Rabanal JE, Aldawsari KA, Aldhaleei WA, Aldridge RW, Alema HB, Alemayohu MA, Alemi S, Alemu YM, Al-Gheethi AAS, Alhabib KF, Alhalaiqa FAN, Al-Hanawi MK, Ali A, Ali A, Ali L, Ali MU, Ali R, Ali S, Ali SSS, Alicandro G, Alif SM, Alikhani R, Alimohamadi Y, Aliyi AA, Aljasir MAM, Aljunid SM, Alla F, Allebeck P, Al-Marwani S, Al-Maweri SAA, Almazan JU, Al-Mekhlafi HM, Almidani L, Almidani O, Alomari MA, Al-Omari B, Alonso J, Alqahtani JS, Alqalyoobi S, Alqutaibi AY, Al-Sabah SK, Altaany Z, Altaf A, Al-Tawfiq JA, Altirkawi KA, Aluh DO, Alvis-Guzman N, Alwafi H, Al-Worafi YM, Aly H, Aly S, Alzoubi KH, Amani R, Amare AT, Amegbor PM, Ameyaw EK, Amin TT, Amindarolzarbi A, Amiri S, Amirzade-Iranaq MH, Amu H, Amugsi DA, Amusa GA, Ancuceanu R, Anderlini D, Anderson DB, Andrade PP, Andrei CL, Andrei T, Angus C, Anil A, Anil S, Anoushiravani A, Ansari H, Ansariadi A, Ansari-Moghaddam A, Antony CM, Antriyandarti E, Anvari D, Anvari S, Anwar S, Anwar SL, Anwer R, Anyasodor AE, Aqeel M, Arab JP, Arabloo J, Arafat M, Aravkin AY, Areda D, Aremu A, Aremu O, Ariffin H, Arkew M, Armocida B, Arndt MB, Ärnlöv J, Arooj M, Artamonov AA, Arulappan J, Aruleba RT, Arumugam A, Asaad M, Asadi-Lari M, Asgedom AA, Asghariahmadabad M, Asghari-Jafarabadi M, Ashraf M, Aslani A, Astell-Burt T, Athar M, Athari SS, Atinafu BTT, Atlaw HW, Atorkey P, Atout MMW, Atreya A, Aujayeb A, Ausloos M, Avan A, Awedew AF, Aweke AM, Ayala Quintanilla BP, Ayatollahi H, Ayuso-Mateos JL, Ayyoubzadeh SM, Azadnajafabad S, Azevedo RMS, Azzam AY, B DB, Babu AS, Badar M, Badiye AD, Baghdadi S, Bagheri N, Bagherieh S, Bah S, Bahadorikhalili S, Bahmanziari N, Bai R, Baig AA, Baker JL, Bako AT, Bakshi RK, Balakrishnan S, Balasubramanian M, Baltatu OC, Bam K, Banach M, Bandyopadhyay S, Banik PC, Bansal H, Bansal K, Barbic F, Barchitta M, Bardhan M, Bardideh E, Barker-Collo SL, Bärnighausen TW, Barone-Adesi F, Barqawi HJ, Barrero LH, Barrow A, Barteit S, Barua L, Basharat Z, Bashiri A, Basiru A, Baskaran P, Basnyat B, Bassat Q, Basso JD, Basting AVL, Basu S, Batra K, Baune BT, Bayati M, Bayileyegn NS, Beaney T, Bedi N, Beghi M, Behboudi E, Behera P, Behnoush AH, Behzadifar M, Beiranvand M, Bejarano Ramirez DF, Béjot Y, Belay SA, Belete CM, Bell ML, Bello MB, Bello OO, Belo L, Beloukas A, Bender RG, Bensenor IM, Beran A, Berezvai Z, Berhie AY, Berice BN, Bernstein RS, Bertolacci GJ, Bettencourt PJG, Beyene KA, Bhagat DS, Bhagavathula AS, Bhala N, Bhalla A, Bhandari D, Bhangdia K, Bhardwaj N, Bhardwaj P, Bhardwaj PV, Bhargava A, Bhaskar S, Bhat V, Bhatti GK, Bhatti JS, Bhatti MS, Bhatti R, Bhutta ZA, Bikbov B, Bishai JD, Bisignano C, Bisulli F, Biswas A, Biswas B, Bitaraf S, Bitew BD, Bitra VR, Bjørge T, Boachie MK, Boampong MS, Bobirca AV, Bodolica V, Bodunrin AO, Bogale EK, Bogale KA, Bohlouli S, Bolarinwa OA, Boloor A, Bonakdar Hashemi M, Bonny A, Bora K, Bora Basara B, Borhany H, Borzutzky A, Bouaoud S, Boustany A, Boxe C, Boyko EJ, Brady OJ, Braithwaite D, Brant LC, Brauer M, Brazinova A, Brazo-Sayavera J, Breitborde NJK, Breitner S, Brenner H, Briko AN, Briko NI, Britton G, Brown J, Brugha T, Bulamu NB, Bulto LN, Buonsenso D, Burns RA, Busse R, Bustanji Y, Butt NS, Butt ZA, Caetano dos Santos FL, Calina D, Cámera LA, Campos LA, Campos-Nonato IR, Cao C, Cao Y, Capodici A, Cárdenas R, Carr S, Carreras G, Carrero JJ, Carugno A, Carvalheiro CG, Carvalho F, Carvalho M, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Catalá-López F, Catapano AL, Cattaruzza MS, Cederroth CR, Cegolon L, Cembranel F, Cenderadewi M, Cercy KM, Cerin E, Cevik M, Chadwick J, Chahine Y, Chakraborty C, Chakraborty PA, Chan JSK, Chan RNC, Chandika RM, Chandrasekar EK, Chang CK, Chang JC, Chanie GS, Charalampous P, Chattu VK, Chaturvedi P, Chatzimavridou-Grigoriadou V, Chaurasia A, Chen AW, Chen AT, Chen CS, Chen H, Chen MX, Chen S, Cheng CY, Cheng ETW, Cherbuin N, Cheru WA, Chien JH, Chimed-Ochir O, Chimoriya R, Ching PR, Chirinos-Caceres JL, Chitheer A, Cho WCS, Chong B, Chopra H, Choudhari SG, Chowdhury R, Christopher DJ, Chukwu IS, Chung E, Chung E, Chung E, Chung SC, Chutiyami M, Cindi Z, Cioffi I, Claassens MM, Claro RM, Coberly K, Cogen RM, Columbus A, Comfort H, Conde J, Cortese S, Cortesi PA, Costa VM, Costanzo S, Cousin E, Couto RAS, Cowden RG, Cramer KM, Criqui MH, Cruz-Martins N, Cuadra-Hernández SM, Culbreth GT, Cullen P, Cunningham M, Curado MP, Dadana S, Dadras O, Dai S, Dai X, Dai Z, Dalli LL, Damiani G, Darega Gela J, Das JK, Das S, Das S, Dascalu AM, Dash NR, Dashti M, Dastiridou A, Davey G, Dávila-Cervantes CA, Davis Weaver N, Davletov K, De Leo D, de Luca K, Debele AT, Debopadhaya S, Degenhardt L, Dehghan A, Deitesfeld L, Del Bo' C, Delgado-Enciso I, Demessa BH, Demetriades AK, Deng K, Deng X, Denova-Gutiérrez E, Deravi N, Dereje N, Dervenis N, Dervišević E, Des Jarlais DC, Desai HD, Desai R, Devanbu VGC, Dewan SMR, Dhali A, Dhama K, Dhimal M, Dhingra S, Dhulipala VR, Dias da Silva D, Diaz D, Diaz MJ, Dima A, Ding DD, Ding H, Dinis-Oliveira RJ, Dirac MA, Djalalinia S, Do THP, do Prado CB, Doaei S, Dodangeh M, Dodangeh M, Dohare S, Dokova KG, Dolecek C, Dominguez RMV, Dong W, Dongarwar D, D'Oria M, Dorostkar F, Dorsey ER, dos Santos WM, Doshi R, Doshmangir L, Dowou RK, Driscoll TR, Dsouza HL, Dsouza V, Du M, Dube J, Duncan BB, Duraes AR, Duraisamy S, Durojaiye OC, Dwyer-Lindgren L, Dzianach PA, Dziedzic AM, E'mar AR, Eboreime E, Ebrahimi A, Echieh CP, Edinur HA, Edvardsson D, Edvardsson K, Efendi D, Efendi F, Effendi DE, Eikemo TA, Eini E, Ekholuenetale M, Ekundayo TC, El Sayed I, Elbarazi I, Elema TB, Elemam NM, Elgar FJ, Elgendy IY, ElGohary GMT, Elhabashy HR, Elhadi M, El-Huneidi W, Elilo LT, Elmeligy OAA, Elmonem MA, Elshaer M, Elsohaby I, Emeto TI, Engelbert Bain L, Erkhembayar R, Esezobor CI, Eshrati B, Eskandarieh S, Espinosa-Montero J, Esubalew H, Etaee F, Fabin N, Fadaka AO, Fagbamigbe AF, Fahim A, Fahimi S, Fakhri-Demeshghieh A, Falzone L, Fareed M, Farinha CSES, Faris MEM, Faris PS, Faro A, Fasanmi AO, Fatehizadeh A, Fattahi H, Fauk NK, Fazeli P, Feigin VL, Feizkhah A, Fekadu G, Feng X, Fereshtehnejad SM, Feroze AH, Ferrante D, Ferrari AJ, Ferreira N, Fetensa G, Feyisa BR, Filip I, Fischer F, Flavel J, Flood D, Florin BT, Foigt NA, Folayan MO, Fomenkov AA, Foroutan B, Foroutan M, Forthun I, Fortuna D, Foschi M, Fowobaje KR, Francis KL, Franklin RC, Freitas A, Friedman J, Friedman SD, Fukumoto T, Fuller JE, Fux B, Gaal PA, Gadanya MA, Gaidhane AM, Gaihre S, Gakidou E, Galali Y, Galles NC, Gallus S, Ganbat M, Gandhi AP, Ganesan B, Ganiyani MA, Garcia-Gordillo MA, Gardner WM, Garg J, Garg N, Gautam RK, Gbadamosi SO, Gebi TG, Gebregergis MW, Gebrehiwot M, Gebremeskel TG, Georgescu SR, Getachew T, Gething PW, Getie M, Ghadiri K, Ghahramani S, Ghailan KY, Ghasemi MR, Ghasempour Dabaghi G, Ghasemzadeh A, Ghashghaee A, Ghassemi F, Ghazy RM, Ghimire A, Ghoba S, Gholamalizadeh M, Gholamian A, Gholamrezanezhad A, Gholizadeh N, Ghorbani M, Ghorbani Vajargah P, Ghoshal AG, Gill PS, Gill TK, Gillum RF, Ginindza TG, Girmay A, Glasbey JC, Gnedovskaya EV, Göbölös L, Godinho MA, Goel A, Golchin A, Goldust M, Golechha M, Goleij P, Gomes NGM, Gona PN, Gopalani SV, Gorini G, Goudarzi H, Goulart AC, Goulart BNG, Goyal A, Grada A, Graham SM, Grivna M, Grosso G, Guan SY, Guarducci G, Gubari MIM, Gudeta MD, Guha A, Guicciardi S, Guimarães RA, Gulati S, Gunawardane DA, Gunturu S, Guo C, Gupta AK, Gupta B, Gupta MK, Gupta M, Gupta RD, Gupta R, Gupta S, Gupta VB, Gupta VK, Gupta VK, Gurmessa L, Gutiérrez RA, Habibzadeh F, Habibzadeh P, Haddadi R, Hadei M, Hadi NR, Haep N, Hafezi-Nejad N, Hailu A, Haj-Mirzaian A, Halboub ES, Hall BJ, Haller S, Halwani R, Hamadeh RR, Hameed S, Hamidi S, Hamilton EB, Han C, Han Q, Hanif A, Hanifi N, Hankey GJ, Hanna F, Hannan MA, Haque MN, Harapan H, Hargono A, Haro JM, Hasaballah AI, Hasan I, Hasan MT, Hasani H, Hasanian M, Hashi A, Hasnain MS, Hassan I, Hassanipour S, Hassankhani H, Haubold J, Havmoeller RJ, Hay SI, He J, Hebert JJ, Hegazi OE, Heidari G, Heidari M, Heidari-Foroozan M, Helfer B, Hendrie D, Herrera-Serna BY, Herteliu C, Hesami H, Hezam K, Hill CL, Hiraike Y, Holla R, Horita N, Hossain MM, Hossain S, Hosseini MS, Hosseinzadeh H, Hosseinzadeh M, Hosseinzadeh Adli A, Hostiuc M, Hostiuc S, Hsairi M, Hsieh VCR, Hsu RL, Hu C, Huang J, Hultström M, Humayun A, Hundie TG, Hussain J, Hussain MA, Hussein NR, Hussien FM, Huynh HH, Hwang BF, Ibitoye SE, Ibrahim KS, Iftikhar PM, Ijo D, Ikiroma AI, Ikuta KS, Ikwegbue PC, Ilesanmi OS, Ilic IM, Ilic MD, Imam MT, Immurana M, Inamdar S, Indriasih E, Iqhrammullah M, Iradukunda A, Iregbu KC, Islam MR, Islam SMS, Islami F, Ismail F, Ismail NE, Iso H, Isola G, Iwagami M, Iwu CCD, Iyamu IO, Iyer M, J LM, Jaafari J, Jacob L, Jacobsen KH, Jadidi-Niaragh F, Jafarinia M, Jafarzadeh A, Jaggi K, Jahankhani K, Jahanmehr N, Jahrami H, Jain N, Jairoun AA, Jaiswal A, Jamshidi E, Janko MM, Jatau AI, Javadov S, Javaheri T, Jayapal SK, Jayaram S, Jebai R, Jee SH, Jeganathan J, Jha AK, Jha RP, Jiang H, Jin Y, Johnson O, Jokar M, Jonas JB, Joo T, Joseph A, Joseph N, Joshua CE, Joshy G, Jozwiak JJ, Jürisson M, K V, Kaambwa B, Kabir A, Kabir Z, Kadashetti V, Kadir DH, Kalani R, Kalankesh LR, Kalankesh LR, Kaliyadan F, Kalra S, Kamal VK, Kamarajah SK, Kamath R, Kamiab Z, Kamyari N, Kanagasabai T, Kanchan T, Kandel H, Kanmanthareddy AR, Kanmiki EW, Kanmodi KK, Kannan S S, Kansal SK, Kantar RS, Kapoor N, Karajizadeh M, Karanth SD, Karasneh RA, Karaye IM, Karch A, Karim A, Karimi SE, Karimi Behnagh A, Kashoo FZ, Kasnazani QHA, Kasraei H, Kassebaum NJ, Kassel MB, Kauppila JH, Kaur N, Kawakami N, Kayode GA, Kazemi F, Kazemian S, Kazmi TH, Kebebew GM, Kebede AD, Kebede F, Keflie TS, Keiyoro PN, Keller C, Kelly JT, Kempen JH, Kerr JA, Kesse-Guyot E, Khajuria H, Khalaji A, Khalid N, Khalil AA, Khalilian A, Khamesipour F, Khan A, Khan A, Khan G, Khan I, Khan IA, Khan MN, Khan M, Khan MJ, Khan MAB, Khan ZA, Khan suheb MZ, Khanmohammadi S, Khatab K, Khatami F, Khatatbeh H, Khatatbeh MM, Khavandegar A, Khayat Kashani HR, Khidri FF, Khodadoust E, Khorgamphar M, Khormali M, Khorrami Z, Khosravi A, Khosravi MA, Kifle ZD, Kim G, Kim J, Kim K, Kim MS, Kim YJ, Kimokoti RW, Kinzel KE, Kisa A, Kisa S, Klu D, Knudsen AKS, Kocarnik JM, Kochhar S, Kocsis T, Koh DSQ, Kolahi AA, Kolves K, Kompani F, Koren G, Kosen S, Kostev K, Koul PA, Koulmane Laxminarayana SL, Krishan K, Krishna H, Krishna V, Krishnamoorthy V, Krishnamoorthy Y, Krohn KJ, Kuate Defo B, Kucuk Bicer B, Kuddus MA, Kuddus M, Kuitunen I, Kulimbet M, Kulkarni V, Kumar A, Kumar A, Kumar H, Kumar M, Kumar R, Kumari M, Kumie FT, Kundu S, Kurmi OP, Kusnali A, Kusuma D, Kwarteng A, Kyriopoulos I, Kyu HH, La Vecchia C, Lacey B, Ladan MA, Laflamme L, Lagat AK, Lager ACJ, Lahmar A, Lai DTC, Lal DK, Lalloo R, Lallukka T, Lam H, Lám J, Landrum KR, Lanfranchi F, Lang JJ, Langguth B, Lansingh VC, Laplante-Lévesque A, Larijani B, Larsson AO, Lasrado S, Lassi ZS, Latief K, Latifinaibin K, Lauriola P, Le NHH, Le TTT, Le TDT, Ledda C, Ledesma JR, Lee M, Lee PH, Lee SW, Lee SWH, Lee WC, Lee YH, LeGrand KE, Leigh J, Leong E, Lerango TL, Li MC, Li W, Li X, Li Y, Li Z, Ligade VS, Likaka ATM, Lim LL, Lim SS, Lindstrom M, Linehan C, Liu C, Liu G, Liu J, Liu R, Liu S, Liu X, Liu X, Llanaj E, Loftus MJ, López-Bueno R, Lopukhov PD, Loreche AM, Lorkowski S, Lotufo PA, Lozano R, Lubinda J, Lucchetti G, Lugo A, Lunevicius R, Ma ZF, Maass KL, Machairas N, Machoy M, Madadizadeh F, Madsen C, Madureira-Carvalho ÁM, Maghazachi AA, Maharaj SB, Mahjoub S, Mahmoud MA, Mahmoudi A, Mahmoudi E, Mahmoudi R, Majeed A, Makhdoom IF, Malakan Rad E, Maled V, Malekzadeh R, Malhotra AK, Malhotra K, Malik AA, Malik I, Malta DC, Mamun AA, Mansouri P, Mansournia MA, Mantovani LG, Maqsood S, Marasini BP, Marateb HR, Maravilla JC, Marconi AM, Mardi P, Marino M, Marjani A, Martinez G, Martinez-Guerra BA, Martinez-Piedra R, Martini D, Martini S, Martins-Melo FR, Martorell M, Marx W, Maryam S, Marzo RR, Masaka A, Masrie A, Mathieson S, Mathioudakis AG, Mathur MR, Mattumpuram J, Matzopoulos R, Maude RJ, Maugeri A, Maulik PK, Mayeli M, Mazaheri M, Mazidi M, McGrath JJ, McKee M, McKowen ALW, McLaughlin SA, McPhail SM, Mechili EA, Medina JRC, Mediratta RP, Meena JK, Mehra R, Mehrabani-Zeinabad K, Mehrabi Nasab E, Mekene Meto T, Meles GG, Mendez-Lopez MAM, Mendoza W, Menezes RG, Mengist B, Mentis AFA, Meo SA, Meresa HA, Meretoja A, Meretoja TJ, Mersha AM, Mesfin BA, Mestrovic T, Mettananda KCD, Mettananda S, Meylakhs P, Mhlanga A, Mhlanga L, Mi T, Miazgowski T, Micha G, Michalek IM, Miller TR, Mills EJ, Minh LHN, Mini GK, Mir Mohammad Sadeghi P, Mirica A, Mirijello A, Mirrakhimov EM, Mirutse MK, Mirzaei M, Misganaw A, Mishra A, Misra S, Mitchell PB, Mithra P, Mittal C, Mobayen M, Moberg ME, Mohamadkhani A, Mohamed J, Mohamed MFH, Mohamed NS, Mohammad-Alizadeh-Charandabi S, Mohammadi S, Mohammadian-Hafshejani A, Mohammadifard N, Mohammed H, Mohammed H, Mohammed M, Mohammed S, Mohammed S, Mohan V, Mojiri-Forushani H, Mokari A, Mokdad AH, Molinaro S, Molokhia M, Momtazmanesh S, Monasta L, Mondello S, Moni MA, Moodi Ghalibaf A, Moradi M, Moradi Y, Moradi-Lakeh M, Moradzadeh M, Moraga P, Morawska L, Moreira RS, Morovatdar N, Morrison SD, Morze J, Mosser JF, Motappa R, Mougin V, Mouodi S, Mousavi P, Mousavi SE, Mousavi Khaneghah A, Mpolya EA, Mrejen M, Mubarik S, Muccioli L, Mueller UO, Mughal F, Mukherjee S, Mulita F, Munjal K, Murillo-Zamora E, Musaigwa F, Musallam KM, Mustafa A, Mustafa G, Muthupandian S, Muthusamy R, Muzaffar M, Myung W, Nagarajan AJ, Nagel G, Naghavi P, Naheed A, Naik GR, Naik G, Nainu F, Nair S, Najmuldeen HHR, Nakhostin Ansari N, Nangia V, Naqvi AA, Narasimha Swamy S, Narayana AI, Nargus S, Nascimento BR, Nascimento GG, Nasehi S, Nashwan AJ, Natto ZS, Nauman J, Naveed M, Nayak BP, Nayak VC, Nazri-Panjaki A, Ndejjo R, Nduaguba SO, Negash H, Negoi I, Negoi RI, Negru SM, Nejadghaderi SA, Nejjari C, Nena E, Nepal S, Ng M, Nggada HA, Nguefack-Tsague G, Ngunjiri JW, Nguyen AH, Nguyen DH, Nguyen HTH, Nguyen PT, Nguyen VT, Niazi RK, Nielsen KR, Nigatu YT, Nikolouzakis TK, Nikoobar A, Nikoomanesh F, Nikpoor AR, Ningrum DNA, Nnaji CA, Nnyanzi LA, Noman EA, Nomura S, Noreen M, Noroozi N, Norrving B, Noubiap JJ, Novotney A, Nri-Ezedi CA, Ntaios G, Ntsekhe M, Nuñez-Samudio V, Nurrika D, Nutor JJ, Oancea B, Obamiro KO, Oboh MA, Odetokun IA, Odogwu NM, O'Donnell MJ, Oduro MS, Ofakunrin AOD, Ogunkoya A, Oguntade AS, Oh IH, Okati-Aliabad H, Okeke SR, Okekunle AP, Okonji OC, Olagunju AT, Olaiya MT, Olatubi MI, Oliveira GMM, Olufadewa II, Olusanya BO, Olusanya JO, Oluwafemi YD, Omar HA, Omar Bali A, Omer GL, Ondayo MA, Ong S, Onwujekwe OE, Onyedibe KI, Ordak M, Orisakwe OE, Orish VN, Ortega-Altamirano DV, Ortiz A, Osman WMS, Ostroff SM, Osuagwu UL, Otoiu A, Otstavnov N, Otstavnov SS, Ouyahia A, Ouyang G, Owolabi MO, Ozten Y, P A MP, Padron-Monedero A, Padubidri JR, Pal PK, Palicz T, Palladino C, Palladino R, Palma-Alvarez RF, Pan F, Pan HF, Pana A, Panda P, Panda-Jonas S, Pandi-Perumal SR, Pangaribuan HU, Panos GD, Panos LD, Pantazopoulos I, Pantea Stoian AM, Papadopoulou P, Parikh RR, Park S, Parthasarathi A, Pashaei A, Pasovic M, Passera R, Pasupula DK, Patel HM, Patel J, Patel SK, Patil S, Patoulias D, Patthipati VS, Paudel U, Pazoki Toroudi H, Pease SA, Peden AE, Pedersini P, Pensato U, Pepito VCF, Peprah EK, Peprah P, Perdigão J, Pereira M, Peres MFP, Perianayagam A, Perico N, Pestell RG, Pesudovs K, Petermann-Rocha FE, Petri WA, Pham HT, Philip AK, Phillips MR, Pierannunzio D, Pigeolet M, Pigott DM, Pilgrim T, Piracha ZZ, Piradov MA, Pirouzpanah S, Plakkal N, Plotnikov E, Podder V, Poddighe D, Polinder S, Polkinghorne KR, Poluru R, Ponkilainen VT, Porru F, Postma MJ, Poudel GR, Pourshams A, Pourtaheri N, Prada SI, Pradhan PMS, Prakasham TN, Prasad M, Prashant A, Prates EJS, Prieto Alhambra D, PRISCILLA TINA, Pritchett N, Purohit BM, Puvvula J, Qasim NH, Qattea I, Qazi AS, Qian G, Qiu S, Qureshi MF, Rabiee Rad M, Radfar A, Radhakrishnan RA, Radhakrishnan V, Raeisi Shahraki H, Rafferty Q, Raggi A, Raghav PR, Raheem N, Rahim F, Rahim MJ, Rahimi-Movaghar V, Rahman MM, Rahman MHU, Rahman M, Rahman MA, Rahmani AM, Rahmani S, Rahmanian V, Rajaa S, Rajput P, Rakovac I, Ramasamy SK, Ramazanu S, Rana K, Ranabhat CL, Rancic N, Rane A, Rao CR, Rao IR, Rao M, Rao SJ, Rasali DP, Rasella D, Rashedi S, Rashedi V, Rashidi MM, Rasouli-Saravani A, Rasul A, Rathnaiah Babu G, Rauniyar SK, Ravangard R, Ravikumar N, Rawaf DL, Rawaf S, Rawal L, Rawassizadeh R, Rawlley B, Raza RZ, Razo C, Redwan EMM, Rehman FU, Reifels L, Reiner Jr RC, Remuzzi G, Reyes LF, Rezaei M, Rezaei N, Rezaei N, Rezaeian M, Rhee TG, Riaz MA, Ribeiro ALP, Rickard J, Riva HR, Robinson-Oden HE, Rodrigues CF, Rodrigues M, Roever L, Rogowski ELB, Rohloff P, Romadlon DS, Romero-Rodríguez E, Romoli M, Ronfani L, Roshandel G, Roth GA, Rout HS, Roy N, Roy P, Rubagotti E, Ruela GDA, Rumisha SF, Runghien T, Rwegerera GM, Rynkiewicz A, S N C, Saad AMA, Saadatian Z, Saber K, Saber-Ayad MM, SaberiKamarposhti M, Sabour S, Sacco S, Sachdev PS, Sachdeva R, Saddik B, Saddler A, Sadee BA, Sadeghi E, Sadeghi E, Sadeghian F, Saeb MR, Saeed U, Safaeinejad F, Safi SZ, Sagar R, Saghazadeh A, Sagoe D, Saheb Sharif-Askari F, Saheb Sharif-Askari N, Sahebkar A, Sahoo SS, Sahoo U, Sahu M, Saif Z, Sajid MR, Sakshaug JW, Salam N, Salamati P, Salami AA, Salaroli LB, Saleh MA, Salehi S, Salem MR, Salem MZY, Salimi S, Samadi Kafil H, Samadzadeh S, Samargandy S, Samodra YL, Samy AM, Sanabria J, Sanna F, Santomauro DF, Santos IS, Santric-Milicevic MM, Sao Jose BP, Sarasmita MA, Saraswathy SYI, Saravanan A, Saravi B, Sarikhani Y, Sarkar T, Sarmiento-Suárez R, Sarode GS, Sarode SC, Sarveazad A, Sathian B, Sathish T, Satpathy M, Sayeed A, Sayeed MA, Saylan M, Sayyah M, Scarmeas N, Schaarschmidt BM, Schlaich MP, Schlee W, Schmidt MI, Schneider IJC, Schuermans A, Schumacher AE, Schutte AE, Schwarzinger M, Schwebel DC, Schwendicke F, Šekerija M, Selvaraj S, Senapati S, Senthilkumaran S, Sepanlou SG, Serban D, Sethi Y, Sha F, Shabany M, Shafaat A, Shafie M, Shah NS, Shah PA, Shah SM, Shahabi S, Shahbandi A, Shahid I, Shahid S, Shahid W, Shahsavari HR, Shahwan MJ, Shaikh A, Shaikh MA, Shakeri A, Shalash AS, Sham S, Shamim MA, Shams-Beyranvand M, Shamshad H, Shamsi MA, Shanawaz M, Shankar A, Sharfaei S, Sharifan A, Sharifi-Rad J, Sharma R, Sharma S, Sharma U, Sharma V, Shastry RP, Shavandi A, Shayan M, Shehabeldine AME, Sheikh A, Sheikhi RA, Shen J, Shetty A, Shetty BSK, Shetty PH, Shi P, Shibuya K, Shiferaw D, Shigematsu M, Shin MJ, Shin YH, Shiri R, Shirkoohi R, Shitaye NA, Shittu A, Shiue I, Shivakumar KM, Shivarov V, Shokraneh F, Shokri A, Shool S, Shorofi SA, Shrestha S, Shuval K, Siddig EE, Silva JP, Silva LMLR, Silva S, Simpson CR, Singal A, Singh A, Singh BB, Singh G, Singh J, Singh NP, Singh P, Singh S, Sinha DN, Sinto R, Siraj MS, Sirota SB, Sitas F, Sivakumar S, Skryabin VY, Skryabina AA, Sleet DA, Socea B, Sokhan A, Solanki R, Solanki S, Soleimani H, Soliman SSM, Song S, Song Y, Sorensen RJD, Soriano JB, Soyiri IN, Spartalis M, Spearman S, Sreeramareddy CT, Srivastava VK, Stanaway JD, Stanikzai MH, Stark BA, Starnes JR, Starodubova AV, Stein C, Stein DJ, Steinbeis F, Steiner C, Steinmetz JD, Steiropoulos P, Stevanović A, Stockfelt L, Stokes MA, Stortecky S, Subramaniyan V, Suleman M, Suliankatchi Abdulkader R, Sultana A, Sun HZ, Sun J, Sundström J, Sunkersing D, Sunnerhagen KS, Swain CK, Szarpak L, Szeto MD, Szócska M, Tabaee Damavandi P, Tabarés-Seisdedos R, Tabatabaei SM, Tabatabaei Malazy O, Tabatabaeizadeh SA, Tabatabai S, Tabish M, TADAKAMADLA JYOTHI, Tadakamadla SK, Taheri Abkenar Y, Taheri Soodejani M, Taiba J, Takahashi K, Talaat IM, Talukder A, Tampa M, Tamuzi JL, Tan KK, Tandukar S, Tang H, Tang HK, Tarigan IU, Tariku MK, Tariqujjaman M, Tarkang EE, Tavakoli Oliaee R, Tavangar SM, Taveira N, Tefera YM, Temsah MH, Temsah RMH, Teramoto M, Tesler R, Teye-Kwadjo E, Thakur R, Thangaraju P, Thankappan KR, Tharwat S, Thayakaran R, Thomas N, Thomas NK, Thomson AM, Thrift AG, Thum CCC, Thygesen LC, Tian J, Tichopad A, Ticoalu JHV, Tillawi T, Tiruye TY, Titova MV, Tonelli M, Topor-Madry R, Toriola AT, Torre AE, Touvier M, Tovani-Palone MR, Tran JT, Tran NM, Trico D, Tromans SJ, Truyen TTTT, Tsatsakis A, Tsegay GM, Tsermpini EE, Tumurkhuu M, Tung K, Tyrovolas S, Uddin SMN, Udoakang AJ, Udoh A, Ullah A, Ullah I, Ullah S, Ullah S, Umakanthan S, Umeokonkwo CD, Unim B, Unnikrishnan B, Unsworth CA, Upadhyay E, Urso D, Usman JS, Vahabi SM, Vaithinathan AG, Valizadeh R, Van de Velde SM, Van den Eynde J, Varga O, Vart P, Varthya SB, Vasankari TJ, Vasic M, Vaziri S, Vellingiri B, Venketasubramanian N, Verghese NA, Verma M, Veroux M, Verras GI, Vervoort D, Villafañe JH, Villanueva GI, Vinayak M, Violante FS, Viskadourou M, Vladimirov SK, Vlassov V, Vo B, Vollset SE, Vongpradith A, Vos T, Vujcic IS, Vukovic R, Wafa HA, Waheed Y, Wamai RG, Wang C, Wang N, Wang S, Wang S, Wang Y, Wang YP, Waqas M, Ward P, Wassie EG, Watson S, Watson SLW, Weerakoon KG, Wei MY, Weintraub RG, Weiss DJ, Westerman R, Whisnant JL, Wiangkham T, Wickramasinghe DP, Wickramasinghe ND, Wilandika A, Wilkerson C, Willeit P, Wilson S, Wojewodzic MW, Woldegebreal DH, Wolf AW, Wolfe CDA, Wondimagegene YA, Wong YJ, Wongsin U, Wu AM, Wu C, Wu F, Wu X, Wu Z, Xia J, Xiao H, Xie Y, Xu S, Xu WD, Xu X, Xu YY, Yadollahpour A, Yamagishi K, Yang D, Yang L, Yano Y, Yao Y, Yaribeygi H, Ye P, Yehualashet SS, Yesiltepe M, Yesuf SA, Yezli S, Yi S, Yigezu A, Yiğit A, Yiğit V, Yip P, Yismaw MB, Yismaw Y, Yon DK, Yonemoto N, Yoon SJ, You Y, Younis MZ, Yousefi Z, Yu C, Yu Y, Yuh FH, Zadey S, Zadnik V, Zafari N, Zakham F, Zaki N, Zaman SB, Zamora N, Zand R, Zangiabadian M, Zar HJ, Zare I, Zarrintan A, Zeariya MGM, Zeinali Z, Zhang H, Zhang J, Zhang J, Zhang L, Zhang Y, Zhang ZJ, Zhao H, Zhong C, Zhou J, Zhu B, Zhu L, Ziafati M, Zielińska M, Zitoun OA, Zoladl M, Zou Z, Zuhlke LJ, Zumla A, Zweck E, Zyoud SH, Wool EE, Murray CJL. Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00367-2. [PMID: 38582094 DOI: 10.1016/s0140-6736(24)00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation.
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Kühnisch J, Aps JK, Splieth C, Lussi A, Jablonski-Momeni A, Mendes FM, Schmalz G, Fontana M, Banerjee A, Ricketts D, Schwendicke F, Douglas G, Campus G, van der Veen M, Opdam N, Doméjean S, Martignon S, Neuhaus KW, Horner K, Huysmans MCD. ORCA-EFCD consensus report on clinical recommendation for caries diagnosis. Paper I: caries lesion detection and depth assessment. Clin Oral Investig 2024; 28:227. [PMID: 38514502 PMCID: PMC10957694 DOI: 10.1007/s00784-024-05597-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/29/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVES The aim of the present consensus paper was to provide recommendations for clinical practice considering the use of visual examination, dental radiography and adjunct methods for primary caries detection. MATERIALS AND METHODS The executive councils of the European Organisation for Caries Research (ORCA) and the European Federation of Conservative Dentistry (EFCD) nominated ten experts each to join the expert panel. The steering committee formed three work groups that were asked to provide recommendations on (1) caries detection and diagnostic methods, (2) caries activity assessment and (3) forming individualised caries diagnoses. The experts responsible for "caries detection and diagnostic methods" searched and evaluated the relevant literature, drafted this manuscript and made provisional consensus recommendations. These recommendations were discussed and refined during the structured process in the whole work group. Finally, the agreement for each recommendation was determined using an anonymous Delphi survey. RESULTS Recommendations (N = 8) were approved and agreed upon by the whole expert panel: visual examination (N = 3), dental radiography (N = 3) and additional diagnostic methods (N = 2). While the quality of evidence was found to be heterogeneous, all recommendations were agreed upon by the expert panel. CONCLUSION Visual examination is recommended as the first-choice method for the detection and assessment of caries lesions on accessible surfaces. Intraoral radiography, preferably bitewing, is recommended as an additional method. Adjunct, non-ionising radiation methods might also be useful in certain clinical situations. CLINICAL RELEVANCE The expert panel merged evidence from the scientific literature with practical considerations and provided recommendations for their use in daily dental practice.
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Affiliation(s)
- Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians Universität München, Poliklinik für Zahnerhaltung und Parodontologie, Goethestraße 70, 80336, München, Germany.
| | | | - Christian Splieth
- Preventive and Pediatric Dentistry, Center for Oral Health, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Adrian Lussi
- University Hospital for Conservative Dentistry and Periodontology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland
| | | | - Fausto M Mendes
- Department of Pediatric Dentistry, School of Dentistry, University of São Paulo, São Paulo, Brazil
| | - Gottfried Schmalz
- Department of Conservative Dentistry and Periodontology, University Hospital Regensburg, Regensburg, Germany
- Department of Periodontology, University of Bern, Bern, Switzerland
| | - Margherita Fontana
- Department of Cariology, Restorative Sciences and Endodontics, University of Michigan School of Dentistry, Ann Arbor, USA
| | - Avijit Banerjee
- Conservative & MI Dentistry, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - David Ricketts
- Unit of Restorative Dentistry, University of Dundee, Dundee, UK
| | - Falk Schwendicke
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians Universität München, Poliklinik für Zahnerhaltung und Parodontologie, Goethestraße 70, 80336, München, Germany
| | - Gail Douglas
- Department of Dental Public Health, University of Leeds Dental School, Leeds, UK
| | - Guglielmo Campus
- Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland
- Department of Surgery, Microsurgery and Medicine Sciences, School of Dentistry, University of Sassari, Sassari, Italy
| | - Monique van der Veen
- Departments of Preventive Dentistry and Paediatric Dentistry, Academic Centre for Dentistry Amsterdam, University of Amsterdam and VU University, Amsterdam, The Netherlands
- Oral Hygiene School, Inholland University of applied sciences, Amsterdam, The Netherlands
| | - Niek Opdam
- Department of Dentistry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sophie Doméjean
- Centre de Recherche en Odontologie Clinique EA 4847, UFR d'Odontologie, Département d'Odontologie Conservatrice, Université Clermont Auvergne, Clermont-Ferrand, France
- Service d'Odontologie, CHU Estaing Clermont-Ferrand, Clermont-Ferrand, France
| | - Stefania Martignon
- UNICA - Caries Research Unit, Research Department, Universidad El Bosque, Bogotá, Colombia
| | - Klaus W Neuhaus
- Department of Pediatric Oral Health, University Center for Dental Medicine Basel (UZB), University of Basel, Basel, Switzerland
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Keith Horner
- Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
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Huth KC, Borkowski L, Liebermann A, Berlinghoff F, Hickel R, Schwendicke F, Reymus M. Comparing accuracy in guided endodontics: dynamic real-time navigation, static guides, and manual approaches for access cavity preparation - an in vitro study using 3D printed teeth. Clin Oral Investig 2024; 28:212. [PMID: 38480541 PMCID: PMC10937753 DOI: 10.1007/s00784-024-05603-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 03/05/2024] [Indexed: 03/17/2024]
Abstract
OBJECTIVES To assess root canal localization accuracy using a dynamic approach, surgical guides and freehand technique in vitro. MATERIALS AND METHODS Access cavities were prepared for 4 different 3D printed tooth types by 4 operators (n = 144). Deviations from the planning in angle and bur positioning were compared and operating time as well as tooth substance loss were evaluated (Kruskal-Wallis Test, ANOVA). Operating method, tooth type, and operator effects were analyzed (partial eta-squared statistic). RESULTS Angle deviation varied significantly between the operating methods (p < .0001): freehand (9.53 ± 6.36°), dynamic (2.82 ± 1.8°) and static navigation (1.12 ± 0.85°). The highest effect size was calculated for operating method (ηP²=0.524), followed by tooth type (0.364), and operator (0.08). Regarding deviation of bur base and tip localization no significant difference was found between the methods. Operating method mainly influenced both parameters (ηP²=0.471, 0.379) with minor effects of tooth type (0.157) and operator. Freehand technique caused most substance loss (p < .001), dynamic navigation least (p < .0001). Operating time was the shortest for freehand followed by static and dynamic navigation. CONCLUSIONS Guided endodontic access may aid in precise root canal localization and save tooth structure. CLINICAL RELEVANCE Although guided endodontic access preparation may require more time compared to the freehand technique, the guided navigation is more accurate and saves tooth structure.
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Affiliation(s)
- Karin Christine Huth
- Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Goethestr. 70, 80336, Munich, Germany.
| | - Lukas Borkowski
- Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Goethestr. 70, 80336, Munich, Germany
| | - Anja Liebermann
- Department of Prosthetic Dentistry, Faculty of Medicine, University of Cologne, University Hospital Cologne, Kerpener Str. 32, 50931, Cologne, Germany
| | - Frank Berlinghoff
- Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Goethestr. 70, 80336, Munich, Germany
| | - Reinhard Hickel
- Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Goethestr. 70, 80336, Munich, Germany
| | - Falk Schwendicke
- Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Goethestr. 70, 80336, Munich, Germany
| | - Marcel Reymus
- Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Goethestr. 70, 80336, Munich, Germany
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Schumacher AE, Kyu HH, Aali A, Abbafati C, Abbas J, Abbasgholizadeh R, Abbasi MA, Abbasian M, Abd ElHafeez S, Abdelmasseh M, Abd-Elsalam S, Abdelwahab A, Abdollahi M, Abdoun M, Abdullahi A, Abdurehman AM, Abebe M, Abedi A, Abedi A, Abegaz TM, Abeldaño Zuñiga RA, Abhilash ES, Abiodun OO, Aboagye RG, Abolhassani H, Abouzid M, Abreu LG, Abrha WA, Abrigo MRM, Abtahi D, Abu Rumeileh S, Abu-Rmeileh NME, Aburuz S, Abu-Zaid A, Acuna JM, Adair T, Addo IY, Adebayo OM, Adegboye OA, Adekanmbi V, Aden B, Adepoju AV, Adetunji CO, Adeyeoluwa TE, Adeyomoye OI, Adha R, Adibi A, Adikusuma W, Adnani QES, Adra S, Afework A, Afolabi AA, Afraz A, Afyouni S, Afzal S, Agasthi P, Aghamiri S, Agodi A, Agyemang-Duah W, Ahinkorah BO, Ahmad A, Ahmad D, Ahmad F, Ahmad MM, Ahmad T, Ahmadi K, Ahmadzade AM, Ahmadzade M, Ahmed A, Ahmed H, Ahmed LA, Ahmed MB, Ahmed SA, Ajami M, Aji B, Ajumobi O, Akalu GT, Akara EM, Akinosoglou K, Akkala S, Akyirem S, Al Hamad H, Al Hasan SM, Al Homsi A, Al Qadire M, Ala M, Aladelusi TO, AL-Ahdal TMA, Alalalmeh SO, Al-Aly Z, Alam K, Alam M, Alam Z, Al-amer RM, Alanezi FM, Alanzi TM, Albashtawy M, AlBataineh MT, Aldridge RW, Alemi S, Al-Eyadhy A, Al-Gheethi AAS, Alhabib KF, Alhalaiqa FAN, Al-Hanawi MK, Ali A, Ali A, Ali BA, Ali H, Ali MU, Ali R, Ali SSS, Ali Z, Alian Samakkhah S, Alicandro G, Alif SM, Aligol M, Alimi R, Aliyi AA, Al-Jumaily A, Aljunid SM, Almahmeed W, Al-Marwani S, Al-Maweri SAA, Almazan JU, Al-Mekhlafi HM, Almidani O, Alomari MA, Alonso N, Alqahtani JS, Alqutaibi AY, Al-Sabah SK, Altaf A, Al-Tawfiq JA, Altirkawi KA, Alvi FJ, Alwafi H, Al-Worafi YM, Aly H, Alzoubi KH, Amare AT, Ameyaw EK, Amhare AF, Amin TT, Amindarolzarbi A, Aminian Dehkordi J, Amiri S, Amu H, Amugsi DA, Amzat J, Ancuceanu R, Anderlini D, Andrade PP, Andrei CL, Andrei T, Angappan D, Anil A, Anjum A, Antony CM, Antriyandarti E, Anuoluwa IA, Anwar SL, Anyasodor AE, Appiah SCY, Aqeel M, Arabloo J, Arabzadeh Bahri R, Arab-Zozani M, Arafat M, Araújo AM, Aravkin AY, Aremu A, Ariffin H, Aripov T, Armocida B, Arooj M, Artamonov AA, Artanti KD, Arulappan J, Aruleba IT, Aruleba RT, Arumugam A, Asaad M, Asgary S, Ashemo MY, Ashraf M, Asika MO, Athari SS, Atout MMW, Atreya A, Attia S, Aujayeb A, Avan A, Awotidebe AW, Ayala Quintanilla BP, Ayanore MA, Ayele GM, Ayuso-Mateos JL, Ayyoubzadeh SM, Azadnajafabad S, Azhar GS, Aziz S, Azzam AY, Babashahi M, Babu AS, Badar M, Badawi A, Badiye AD, Baghdadi S, Bagheri N, Bagherieh S, Bah S, Bahadorikhalili S, Bai J, Bai R, Baker JL, Bakkannavar SM, Bako AT, Balakrishnan S, Balogun SA, Baltatu OC, Bam K, Banach M, Bandyopadhyay S, Banik B, Banik PC, Bansal H, Barati S, Barchitta M, Bardhan M, Barker-Collo SL, Barone-Adesi F, Barqawi HJ, Barr RD, Barrero LH, Basharat Z, Bashir AIJ, Bashiru HA, Baskaran P, Basnyat B, Bassat Q, Basso JD, Basu S, Batra K, Batra R, Baune BT, Bayati M, Bayileyegn NS, Beaney T, Bedi N, Begum T, Behboudi E, Behnoush AH, Beiranvand M, Bejarano Ramirez DF, Belgaumi UI, Bell ML, Bello AK, Bello MB, Bello OO, Belo L, Beloukas A, Bendak S, Bennett DA, Bensenor IM, Benzian H, Berezvai Z, Berman AE, Bermudez ANC, Bettencourt PJG, Beyene HB, Beyene KA, Bhagat DS, Bhagavathula AS, Bhala N, Bhalla A, Bhandari D, Bhardwaj N, Bhardwaj P, Bhardwaj PV, Bhargava A, Bhaskar S, Bhat V, Bhatti GK, Bhatti JS, Bhatti MS, Bhatti R, Bhutta ZA, Bikbov B, Binmadi N, Bintoro BS, Biondi A, Bisignano C, Bisulli F, Biswas A, Biswas RK, Bitaraf S, Bjørge T, Bleyer A, Boampong MS, Bodolica V, Bodunrin AO, Bolarinwa OA, Bonakdar Hashemi M, Bonny A, Bora K, Bora Basara B, Borodo SB, Borschmann R, Botero Carvajal A, Bouaoud S, Boudalia S, Boyko EJ, Bragazzi NL, Braithwaite D, Brenner H, Britton G, Browne AJ, Brunoni AR, Bulamu NB, Bulto LN, Buonsenso D, Burkart K, Burns RA, Burugina Nagaraja S, Busse R, Bustanji Y, Butt ZA, Caetano dos Santos FL, Cai T, Calina D, Cámera LA, Campos LA, Campos-Nonato IR, Cao C, Cardenas CA, Cárdenas R, Carr S, Carreras G, Carrero JJ, Carugno A, Carvalho F, Carvalho M, Castaldelli-Maia JM, Castañeda-Orjuela CA, Castelpietra G, Catalá-López F, Catapano AL, Cattaruzza MS, Caye A, Cederroth CR, Cembranel F, Cenderadewi M, Cercy KM, Cerin E, Cevik M, Chacón-Uscamaita PRU, Chahine Y, Chakraborty C, Chan JSK, Chang CK, Charalampous P, Charan J, Chattu VK, Chatzimavridou-Grigoriadou V, Chavula MP, Cheema HA, Chen AT, Chen H, Chen L, Chen MX, Chen S, Cherbuin N, Chew DS, Chi G, Chirinos-Caceres JL, Chitheer A, Cho SMJ, Cho WCS, Chong B, Chopra H, Choudhary R, Chowdhury R, Chu DT, Chukwu IS, Chung E, Chung E, Chung SC, Cini KI, Clark CCT, Coberly K, Columbus A, Comfort H, Conde J, Conti S, Cortesi PA, Costa VM, Cousin E, Cowden RG, Criqui MH, Cruz-Martins N, Culbreth GT, Cullen P, Cunningham M, da Silva e Silva D, Dadana S, Dadras O, Dai Z, Dalal K, Dalli LL, Damiani G, D'Amico E, Daneshvar S, Darwesh AM, Das JK, Das S, Dash NR, Dashti M, Dávila-Cervantes CA, Davis Weaver N, Davletov K, De Leo D, Debele AT, Degenhardt L, Dehbandi R, Deitesfeld L, Delgado-Enciso I, Delgado-Ortiz L, Demant D, Demessa BH, Demetriades AK, Deng X, Denova-Gutiérrez E, Deribe K, Dervenis N, Des Jarlais DC, Desai HD, Desai R, Deuba K, Devanbu VGC, Dey S, Dhali A, Dhama K, Dhimal ML, Dhimal M, Dhingra S, Dias da Silva D, Diaz D, Dima A, Ding DD, Dirac MA, Dixit A, Dixit SG, Do TC, Do THP, do Prado CB, Dodangeh M, Dokova KG, Dolecek C, Dorsey ER, dos Santos WM, Doshi R, Doshmangir L, Douiri A, Dowou RK, Driscoll TR, Dsouza HL, Dube J, Dumith SC, Dunachie SJ, Duncan BB, Duraes AR, Duraisamy S, Durojaiye OC, Dutta S, Dzianach PA, Dziedzic AM, Ebenezer O, Eboreime E, Ebrahimi A, Echieh CP, Ed-Dra A, Edinur HA, Edvardsson D, Edvardsson K, Efendi D, Efendi F, Eghdami S, Eikemo TA, Eini E, Ekholuenetale M, Ekpor E, Ekundayo TC, El Arab RA, El Morsi DAW, El Sayed Zaki M, El Tantawi M, Elbarazi I, Elemam NM, Elgar FJ, Elgendy IY, ElGohary GMT, Elhabashy HR, Elhadi M, Elmeligy OAA, Elshaer M, Elsohaby I, Emami Zeydi A, Emamverdi M, Emeto TI, Engelbert Bain L, Erkhembayar R, Eshetie TC, Eskandarieh S, Espinosa-Montero J, Estep K, Etaee F, Eze UA, Fabin N, Fadaka AO, Fagbamigbe AF, Fahimi S, Falzone L, Farinha CSES, Faris MEM, Farjoud Kouhanjani M, Faro A, Farrokhpour H, Fatehizadeh A, Fattahi H, Fauk NK, Fazeli P, Feigin VL, Fekadu G, Fereshtehnejad SM, Feroze AH, Ferrante D, Ferrara P, Ferreira N, Fetensa G, Filip I, Fischer F, Flavel J, Flaxman AD, Flor LS, Florin BT, Folayan MO, Foley KM, Fomenkov AA, Force LM, Fornari C, Foroutan B, Foschi M, Francis KL, Franklin RC, Freitas A, Friedman J, Friedman SD, Fukumoto T, Fuller JE, Gaal PA, Gadanya MA, Gaihre S, Gaipov A, Gakidou E, Galali Y, Galehdar N, Gallus S, Gan Q, Gandhi AP, Ganesan B, Garg J, Gau SY, Gautam P, Gautam RK, Gazzelloni F, Gebregergis MW, Gebrehiwot M, Gebremariam TB, Gerema U, Getachew ME, Getachew T, Gething PW, Ghafourifard M, Ghahramani S, Ghailan KY, Ghajar A, Ghanbarnia MJ, Ghasemi M, Ghasemzadeh A, Ghassemi F, Ghazy RM, Ghimire S, Gholamian A, Gholamrezanezhad A, Ghorbani Vajargah P, Ghozali G, Ghozy S, Ghuge AD, Gialluisi A, Gibson RM, Gil AU, Gill PS, Gill TK, Gillum RF, Ginindza TG, Girmay A, Glasbey JC, Gnedovskaya EV, Göbölös L, Goel A, Goldust M, Golechha M, Goleij P, Golestanfar A, Golinelli D, Gona PN, Goudarzi H, Goudarzian AH, Goyal A, Greenhalgh S, Grivna M, Guarducci G, Gubari MIM, Gudeta MD, Guha A, Guicciardi S, Gunawardane DA, Gunturu S, Guo C, Gupta AK, Gupta B, Gupta IR, Gupta RD, Gupta S, Gupta VB, Gupta VK, Gupta VK, Gutiérrez RA, Habibzadeh F, Habibzadeh P, Hachinski V, Haddadi M, Haddadi R, Haep N, Hajj Ali A, Halboub ES, Halim SA, Hall BJ, Haller S, Halwani R, Hamadeh RR, Hamagharib Abdullah K, Hamidi S, Hamiduzzaman M, Hammoud A, Hanifi N, Hankey GJ, Hannan MA, Haque MN, Harapan H, Haro JM, Hasaballah AI, Hasan F, Hasan I, Hasan MT, Hasani H, Hasanian M, Hasanpour- Dehkordi A, Hassan AM, Hassan A, Hassanian-Moghaddam H, Hassanipour S, Haubold J, Havmoeller RJ, Hay SI, Hbid Y, Hebert JJ, Hegazi OE, Heidari G, Heidari M, Heidari-Foroozan M, Heidari-Soureshjani R, Helfer B, Herteliu C, Hesami H, Hettiarachchi D, Heyi DZ, Hezam K, Hiraike Y, Hoffman HJ, Holla R, Horita N, Hossain MB, Hossain MM, Hossain S, Hosseini MS, Hosseinzadeh H, Hosseinzadeh M, Hostiuc M, Hostiuc S, Hsairi M, Hsieh VCR, Hu C, Huang J, Huda MN, Hugo FN, Hultström M, Hussain J, Hussain S, Hussein NR, Huy LD, Huynh HH, Hwang BF, Ibitoye SE, Idowu OO, Ijo D, Ikuta KS, Ilaghi M, Ilesanmi OS, Ilic IM, Ilic MD, Immurana M, Inbaraj LR, Iradukunda A, Iravanpour F, Iregbu KC, Islam MR, Islam MM, Islam SMS, Islami F, Ismail NE, Isola G, Iwagami M, Iwu CCD, Iwu-Jaja CJ, Iyer M, J LM, Jaafari J, Jacob L, Jacobsen KH, Jadidi-Niaragh F, Jafarinia M, Jaggi K, Jahankhani K, Jahanmehr N, Jahrami H, Jain A, Jain N, Jairoun AA, Jakovljevic M, Jalilzadeh Yengejeh R, Jamshidi E, Jani CT, Janko MM, Jatau AI, Jayapal SK, Jayaram S, Jeganathan J, Jema AT, Jemere DM, Jeong W, Jha AK, Jha RP, Ji JS, Jiang H, Jin Y, Jin Y, Johnson O, Jomehzadeh N, Jones DP, Joo T, Joseph A, Joseph N, Joshua CE, Jozwiak JJ, Jürisson M, Kaambwa B, Kabir A, Kabir H, Kabir Z, Kadashetti V, Kahe F, Kakodkar PV, Kalani R, Kalankesh LR, Kaliyadan F, Kalra S, Kamath A, Kamireddy A, Kanagasabai T, Kandel H, Kanmiki EW, Kanmodi KK, Kantar RS, Kapoor N, Karajizadeh M, Karami Matin B, Karanth SD, Karaye IM, Karim A, Karimi H, Karimi SE, Karimi Behnagh A, Karkhah S, Karna AK, Kashoo FZ, Kasraei H, Kassaw NA, Kassebaum NJ, Kassel MB, Katamreddy A, Katikireddi SV, Katoto PDMC, Kauppila JH, Kaur N, Kaydi N, Kayibanda JF, Kayode GA, Kazemi F, Kazemian S, Kazeminia S, Keikavoosi-Arani L, Keller C, Kempen JH, Kerr JA, Kesse-Guyot E, Keykhaei M, Khadembashiri MM, Khadembashiri MA, Khafaie MA, Khajuria H, Khalafi M, Khalaji A, Khalid N, Khalil IA, Khamesipour F, Khan A, Khan G, Khan I, Khan IA, Khan M, Khan MAB, Khan T, Khan suheb MZ, Khanmohammadi S, Khatab K, Khatami F, Khavandegar A, Khayat Kashani HR, Kheirallah KA, Khidri FF, Khodadoust E, Khormali M, Khosrowjerdi M, Khubchandani J, Khusun H, Kifle ZD, Kim G, Kim J, Kimokoti RW, Kinzel KE, Kiross GT, Kisa A, Kisa S, Kiss JB, Kivimäki M, Klu D, Knudsen AKS, Kolahi AA, Kompani F, Koren G, Kosen S, Kostev K, Kotnis AL, Koul PA, Koulmane Laxminarayana SL, Koyanagi A, Kravchenko MA, Krishan K, Krishna H, Krishnamoorthy V, Krishnamoorthy Y, Krohn KJ, Kuate Defo B, Kubeisy CM, Kucuk Bicer B, Kuddus MA, Kuddus M, Kuitunen I, Kujan O, Kulimbet M, Kulkarni V, Kumar A, Kumar H, Kumar N, Kumar R, Kumar S, Kumari M, Kurmanova A, Kurmi OP, Kusnali A, Kusuma D, Kutluk T, Kuttikkattu A, Kyei EF, Kyriopoulos I, La Vecchia C, Ladan MA, Laflamme L, Lahariya C, Lahmar A, Lai DTC, Laksono T, Lal DK, Lalloo R, Lallukka T, Lám J, Lamnisos D, Lan T, Lanfranchi F, Langguth B, Lansingh VC, Laplante-Lévesque A, Larijani B, Larsson AO, Lasrado S, Latief K, Latif M, Latifinaibin K, Lauriola P, Le LKD, Le NHH, Le TTT, Le TDT, Lee M, Lee PH, Lee SW, Lee SW, Lee WC, Lee YH, Legesse SM, Leigh J, Lenzi J, Leong E, Lerango TL, Li MC, Li W, Li X, Li Y, Li Z, Libra M, Ligade VS, Likaka ATM, Lim LL, Lin RT, Lin S, Lioutas VA, Listl S, Liu J, Liu S, Liu X, Livingstone KM, Llanaj E, Lo CH, Loreche AM, Lorenzovici L, Lotfi M, Lotfizadeh M, Lozano R, Lubinda J, Lucchetti G, Lugo A, Lunevicius R, Ma J, Ma S, Ma ZF, Mabrok M, Machairas N, Machoy M, Madsen C, Magaña Gómez JA, Maghazachi AA, Maharaj SB, Maharjan P, Mahjoub S, Mahmoud MA, Mahmoudi E, Mahmoudi M, Makram OM, Malagón-Rojas JN, Malakan Rad E, Malekzadeh R, Malhotra AK, Malhotra K, Malik AA, Malik I, Malinga LA, Malta DC, Mamun AA, Manla Y, Mannan F, Mansoori Y, Mansour A, Mansouri V, Mansournia MA, Mantovani LG, Marasini BP, Marateb HR, Maravilla JC, Marconi AM, Mardi P, Marino M, Marjani A, Marrugo Arnedo CA, Martinez-Guerra BA, Martinez-Piedra R, Martins CA, Martins-Melo FR, Martorell M, Marx W, Maryam S, Marzo RR, Mate KKV, Matei CN, Mathioudakis AG, Maude RJ, Maugeri A, May EA, Mayeli M, Mazaheri M, Mazidi M, Mazzotti A, McAlinden C, McGrath JJ, McKee M, McKowen ALW, McLaughlin SA, McPhail MA, McPhail SM, Mechili EA, Mediratta RP, Meena JK, Mehari M, Mehlman ML, Mehra R, Mehrabani-Zeinabad K, Mehrabi Nasab E, Mehrotra R, Mekonnen MM, Mendoza W, Menezes RG, Mengesha EW, Mensah GA, Mensah LG, Mentis AFA, Meo SA, Meretoja A, Meretoja TJ, Mersha AM, Mesfin BA, Mestrovic T, Mhlanga A, Mhlanga L, Mi T, Micha G, Michalek IM, Miller TR, Mindlin SN, Minelli G, Minh LHN, Mini GK, Minja NW, Mirdamadi N, Mirghafourvand M, Mirica A, Mirinezhad SK, Mirmosayyeb O, Mirutse MK, Mirza-Aghazadeh-Attari M, Mirzaei M, Misgana T, Misra S, Mitchell PB, Mithra P, Mittal C, Mittal M, Moazen B, Mohamed AI, Mohamed J, Mohamed MFH, Mohamed NS, Mohammad-Alizadeh-Charandabi S, Mohammadi S, Mohammadian-Hafshejani A, Mohammad-pour S, Mohammadshahi M, Mohammed M, Mohammed S, Mohammed S, Mojiri-forushani H, Mokdad AH, Mokhtarzadehazar P, Momenzadeh K, Momtazmanesh S, Monasta L, Moni MA, Montazeri F, Moodi Ghalibaf A, Moradi M, Moradi Y, Moradi-Lakeh M, Moradinazar M, Moradpour F, Moraga P, Morawska L, Moreira RS, Morovatdar N, Morrison SD, Morze J, Mosaddeghi Heris R, Mosser JF, Mossialos E, Mostafavi H, Mostofinejad A, Mougin V, Mouodi S, Mousavi P, Mousavi SE, Mousavi Khaneghah A, Mpundu-Kaambwa C, Mrejen M, Mubarik S, Muccioli L, Mueller UO, Mughal F, Mukherjee S, Mukoro GD, Mulita A, Mulita F, Muniyandi M, Munjal K, Musaigwa F, Musallam KM, Mustafa G, Muthu S, Muthupandian S, Myung W, Nabhan AF, Nafukho FM, Nagarajan AJ, Naghavi M, Naghavi P, Naik GR, Naik G, Naimzada MD, Nair S, Nair TS, Najmuldeen HHR, Naldi L, Nangia V, Nargus S, Nascimento BR, Nascimento GG, Naser AY, Nasiri MJ, Natto ZS, Nauman J, Naveed M, Nayak BP, Nayak VC, Nayyar AK, Nazri-Panjaki A, Negash H, Negero AK, Negoi I, Negoi RI, Negru SM, Nejadghaderi SA, Nejjari C, Nematollahi MH, Nena E, Nepal S, Nesbit OD, Newton CRJ, Ngunjiri JW, Nguyen DH, Nguyen PT, Nguyen PT, Nguyen TT, Nguyen VT, Nigatu YT, Nikolouzakis TK, Nikoobar A, Nikpoor AR, Nizam MA, Nomura S, Noreen M, Noroozi N, Norouzian Baghani A, Norrving B, Noubiap JJ, Novotney A, Nri-Ezedi CA, Ntaios G, Ntsekhe M, Nuñez-Samudio V, Nurrika D, Oancea B, Obamiro KO, Odetokun IA, Ofakunrin AOD, Ogunsakin RE, Oguta JO, Oh IH, Okati-Aliabad H, Okeke SR, Okekunle AP, Okidi L, Okonji OC, Okwute PG, Olagunju AT, Olaiya MT, Olanipekun TO, Olatubi MI, Olivas-Martinez A, Oliveira GMM, Oliver S, Olorukooba AA, Olufadewa II, Olusanya BO, Olusanya JO, Oluwafemi YD, Oluwatunase GO, Omar HA, Omer GL, Ong S, Onwujekwe OE, Onyedibe KI, Opio JN, Ordak M, Orellana ER, Orisakwe OE, Orish VN, Orru H, Ortega-Altamirano DV, Ortiz A, Ortiz-Brizuela E, Ortiz-Prado E, Osuagwu UL, Otoiu A, Otstavnov N, Ouyahia A, Ouyang G, Owolabi MO, Oyeyemi IT, Oyeyemi OT, Ozten Y, P A MP, Padubidri JR, Pahlavikhah Varnosfaderani M, Pal PK, Palicz T, Palladino C, Palladino R, Palma-Alvarez RF, Pana A, Panahi P, Pandey A, Pandi-Perumal SR, Pando-Robles V, Pangaribuan HU, Panos GD, Pantazopoulos I, Papadopoulou P, Pardhan S, Parikh RR, Park S, Parthasarathi A, Pashaei A, Pasupula DK, Patel JR, Patel SK, Pathan AR, Patil A, Patil S, Patoulias D, Patthipati VS, Paudel U, Pawar S, Pazoki Toroudi H, Pease SA, Peden AE, Pedersini P, Peng M, Pensato U, Pepito VCF, Peprah EK, Pereira G, Pereira J, Pereira M, Peres MFP, Perianayagam A, Perico N, Petcu IR, Petermann-Rocha FE, Pezzani R, Pham HT, Phillips MR, Pierannunzio D, Pigeolet M, Pigott DM, Pilgrim T, Pinheiro M, Piradov MA, Plakkal N, Plotnikov E, Poddighe D, Pollner P, Poluru R, Pond CD, Postma MJ, Poudel GR, Poudel L, Pourali G, Pourtaheri N, Prada SI, Pradhan PMS, Prajapati VK, Prakash V, Prasad CP, Prasad M, Prashant A, Prates EJS, Purnobasuki H, Purohit BM, Puvvula J, Qaisar R, Qasim NH, Qattea I, Qian G, Quan NK, Radfar A, Radhakrishnan V, Raee P, Raeisi Shahraki H, Rafiei Alavi SN, Rafique I, Raggi A, Rahim F, Rahman MM, Rahman M, Rahman MA, Rahman T, Rahmani AM, Rahmani S, Rahnavard N, Rai P, Rajaa S, Rajabpour-Sanati A, Rajput P, Ram P, Ramadan H, Ramasamy SK, Ramazanu S, Rana J, Rana K, Ranabhat CL, Rancic N, Rani S, Ranjan S, Rao CR, Rao IR, Rao M, Rao SJ, Rasali DP, Rasella D, Rashedi S, Rashedi V, Rashid AM, Rasouli-Saravani A, Rastogi P, Rasul A, Ravangard R, Ravikumar N, Rawaf DL, Rawaf S, Rawassizadeh R, Razeghian-Jahromi I, Reddy MMRK, Redwan EMM, Rehman FU, Reiner Jr RC, Remuzzi G, Reshmi B, Resnikoff S, Reyes LF, Rezaee M, Rezaei N, Rezaei N, Rezaeian M, Riaz MA, Ribeiro AI, Ribeiro DC, Rickard J, Rios-Blancas MJ, Robinson-Oden HE, Rodrigues M, Rodriguez JAB, Roever L, Rohilla R, Rohloff P, Romadlon DS, Ronfani L, Roshandel G, Roshanzamir S, Rostamian M, Roy B, Roy P, Rubagotti E, Rumisha SF, Rwegerera GM, Rynkiewicz A, S M, S N C, S Sunnerhagen K, Saad AMA, Sabbatucci M, Saber K, Saber-Ayad MM, Sacco S, Saddik B, Saddler A, Sadee BA, Sadeghi E, Sadeghi M, Sadeghian S, Saeed U, Saeedi M, Safi S, Sagar R, Saghazadeh A, Saheb Sharif-Askari N, Sahoo SS, Sahraian MA, Sajedi SA, Sajid MR, Sakshaug JW, Salahi S, Salahi S, Salamati P, Salami AA, Salaroli LB, Saleh MA, Salehi S, Salem MR, Salem MZY, Salimi S, Samadi Kafil H, Samadzadeh S, Samara KA, Samargandy S, Samodra YL, Samuel VP, Samy AM, Sanabria J, Sanadgol N, Sanganyado E, Sanjeev RK, Sanmarchi F, Sanna F, Santri IN, Santric-Milicevic MM, Sarasmita MA, Saravanan A, Saravi B, Sarikhani Y, Sarkar C, Sarmiento-Suárez R, Sarode GS, Sarode SC, Sarveazad A, Sathian B, Sathish T, Sattin D, Saulam J, Sawyer SM, Saxena S, Saya GK, Sayadi Y, Sayeed A, Sayeed MA, Saylan M, Scarmeas N, Schaarschmidt BM, Schlee W, Schmidt MI, Schuermans A, Schwebel DC, Schwendicke F, Šekerija M, Selvaraj S, Semreen MH, Senapati S, Sengupta P, Senthilkumaran S, Sepanlou SG, Serban D, Sertsu A, Sethi Y, SeyedAlinaghi S, Seyedi SA, Shafaat A, Shafaat O, Shafie M, Shafiee A, Shah NS, Shah PA, Shahabi S, Shahbandi A, Shahid I, Shahid S, Shahid W, Shahwan MJ, Shaikh MA, Shakeri A, Shakil H, Sham S, Shamim MA, Shams-Beyranvand M, Shamshad H, Shamshirgaran MA, Shamsi MA, Shanawaz M, Shankar A, Sharfaei S, Sharifan A, Shariff M, Sharifi-Rad J, Sharma M, Sharma R, Sharma S, Sharma V, Shastry RP, Shavandi A, Shaw DH, Shayan AM, Shehabeldine AME, Sheikh A, Sheikhi RA, Shen J, Shenoy MM, Shetty BSK, Shetty RS, Shey RA, Shiani A, Shibuya K, Shiferaw D, Shigematsu M, Shin JI, Shin MJ, Shiri R, Shirkoohi R, Shittu A, Shiue I, Shivakumar KM, Shivarov V, Shool S, Shrestha S, Shuja KH, Shuval K, Si Y, Sibhat MM, Siddig EE, Sigfusdottir ID, Silva JP, Silva LMLR, Silva S, Simões JP, Simpson CR, Singal A, Singh A, Singh A, Singh A, Singh BB, Singh B, Singh M, Singh M, Singh NP, Singh P, Singh S, Siraj MS, Sitas F, Sivakumar S, Skryabin VY, Skryabina AA, Sleet DA, Slepak ELN, Sohrabi H, Soleimani H, Soliman SSM, Solmi M, Solomon Y, Song Y, Sorensen RJD, Soriano JB, Soyiri IN, Spartalis M, Sreeramareddy CT, Starnes JR, Starodubov VI, Starodubova AV, Stefan SC, Stein DJ, Steinbeis F, Steiropoulos P, Stockfelt L, Stokes MA, Stortecky S, Stranges S, Stroumpoulis K, Suleman M, Suliankatchi Abdulkader R, Sultana A, Sun J, Sunkersing D, Susanty S, Swain CK, Sykes BL, Szarpak L, Szeto MD, Szócska M, Tabaee Damavandi P, Tabatabaei Malazy O, Tabatabaeizadeh SA, Tabatabai S, Tabb KM, Tabish M, Taborda-Barata LM, Tabuchi T, Tadesse BT, Taheri A, Taheri Abkenar Y, Taheri Soodejani M, Taherkhani A, Taiba J, Tajbakhsh A, Talaat IM, Talukder A, Tamuzi JL, Tan KK, Tang H, Tang HK, Tat NY, Tat VY, Tavakoli Oliaee R, Tavangar SM, Taveira N, Tebeje TM, Tefera YM, Teimoori M, Temsah MH, Temsah RMH, Teramoto M, Tesfaye SH, Thangaraju P, Thankappan KR, Thapa R, Thapar R, Thomas N, Thrift AG, Thum CCC, Tian J, Tichopad A, Ticoalu JHV, Tiruye TY, Tohidast SA, Tonelli M, Touvier M, Tovani-Palone MR, Tram KH, Tran NM, Trico D, Trihandini I, Tromans SJ, Truong VT, Truyen TTTT, Tsermpini EE, Tumurkhuu M, Tung K, Tyrovolas S, Ubah CS, Udoakang AJ, Udoh A, Ulhaq I, Ullah S, Ullah S, Umair M, Umar TP, Umeokonkwo CD, Umesh A, Unim B, Unnikrishnan B, Upadhyay E, Urso D, Vacante M, Vahdani AM, Vaithinathan AG, Valadan Tahbaz S, Valizadeh R, Van den Eynde J, Varavikova E, Varga O, Varma SA, Vart P, Varthya SB, Vasankari TJ, Veerman LJ, Venketasubramanian N, Venugopal D, Verghese NA, Verma M, Verma P, Veroux M, Verras GI, Vervoort D, Vieira RJ, Villafañe JH, Villani L, Villanueva GI, Villeneuve PJ, Violante FS, Visontay R, Vlassov V, Vo B, Vollset SE, Volovat SR, Volovici V, Vongpradith A, Vos T, Vujcic IS, Vukovic R, Wado YD, Wafa HA, Waheed Y, Wamai RG, Wang C, Wang D, Wang F, Wang S, Wang S, Wang Y, Wang YP, Ward P, Watson S, Weaver MR, Weerakoon KG, Weiss DJ, Weldemariam AH, Wells KM, Wen YF, Werdecker A, Westerman R, Wickramasinghe DP, Wickramasinghe ND, Wijeratne T, Wilson S, Wojewodzic MW, Wool EE, Woolf AD, Wu D, Wulandari RD, Xiao H, Xu B, Xu X, Yadav L, Yaghoubi S, Yang L, Yano Y, Yao Y, Ye P, Yesera GE, Yesodharan R, Yesuf SA, Yiğit A, Yiğit V, Yip P, Yon DK, Yonemoto N, You Y, Younis MZ, Yu C, Zadey S, Zadnik V, Zafari N, Zahedi M, Zahid MN, Zahir M, Zakham F, Zaki N, Zakzuk J, Zamagni G, Zaman BA, Zaman SB, Zamora N, Zand R, Zandi M, Zandieh GGZ, Zanghì A, Zare I, Zastrozhin MS, Zeariya MGM, Zeng Y, Zhai C, Zhang C, Zhang H, Zhang H, Zhang Y, Zhang Z, Zhang Z, Zhao H, Zhao Y, Zhao Y, Zheng P, Zhong C, Zhou J, Zhu B, Zhu Z, Ziaeefar P, Zielińska M, Zou Z, Zumla A, Zweck E, Zyoud SH, Lim SS, Murray CJL. Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00476-8. [PMID: 38484753 DOI: 10.1016/s0140-6736(24)00476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/08/2023] [Accepted: 03/06/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period. METHODS 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING Bill & Melinda Gates Foundation.
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Geiken A, Kock M, Banz L, Schwendicke F, Graetz C. Dental Practice Websites in Germany-How Do They Inform about Fluoridation? Dent J (Basel) 2024; 12:65. [PMID: 38534289 DOI: 10.3390/dj12030065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 02/25/2024] [Accepted: 02/29/2024] [Indexed: 03/28/2024] Open
Abstract
Fluoridation (Fl) is effective in preventing caries; however, it is unclear to what extent its use is counteracted by misinformation on the internet. This study aimed to evaluate the information provided on professional websites of German dental practices regarding fluoridation. A systematic search was performed by two independent examiners, utilizing three search engines, from 10 September 2021 to 11 December 2021. Modified, validated questionnaires (LIDA, DISCERN) were used to evaluate technical and functional aspects, generic quality, and risk of bias. Demographic information and statements about Fl were also collected. The intra- and inter-rater reliability assessments were excellent. Of the 81 websites analyzed, 64 (79%) mentioned Fl, and 31 (38%) indicated it as a primary focus. Most websites met at least 50% of the LIDA (90%) and DISCERN criteria (99%), indicating that the general quality was good. Thirty (37%) of the websites explained the impact of Fl, and forty-five (56%) indicated an opinion (for/against) on Fl. The practice location and the clinical focus were not associated with the overall quality of websites. Only a minority of websites explained the effects of Fl. Taken together, this study highlights that there is a distinct lack of good-quality information on FL.
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Affiliation(s)
- Antje Geiken
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, 24105 Kiel, Germany
| | - Mirja Kock
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, 24105 Kiel, Germany
| | - Lisa Banz
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, 24105 Kiel, Germany
| | - Falk Schwendicke
- Clinic for Conservative Dentistry and Periodontology, University Hospital of Ludwig-Maximilians-University Munich, 80336 Munich, Germany
| | - Christian Graetz
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, 24105 Kiel, Germany
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Mariño RJ, Uribe SE, Chen R, Schwendicke F, Giraudeau N, Scheerman JFM. Terminology of e-Oral Health: Consensus Report of the IADR's e-Oral Health Network Terminology Task Force. BMC Oral Health 2024; 24:280. [PMID: 38419003 PMCID: PMC10900602 DOI: 10.1186/s12903-024-03929-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 01/23/2024] [Indexed: 03/02/2024] Open
Abstract
OBJECTIVE Authors reported multiple definitions of e-oral health and related terms, and used several definitions interchangeably, like mhealth, teledentistry, teleoral medicine and telehealth. The International Association of Dental Research e-Oral Health Network (e-OHN) aimed to establish a consensus on terminology related to digital technologies used in oral healthcare. METHOD The Crowdsourcing Delphi method used in this study comprised of four main stages. In the first stage, the task force created a list of terms and definitions around digital health technologies based on the literature and established a panel of experts. Inclusion criteria for the panellists were: to be actively involved in either research and/or working in e-oral health fields; and willing to participate in the consensus process. In the second stage, an email-based consultation was organized with the panel of experts to confirm an initial set of terms. In the third stage, consisted of: a) an online meeting where the list of terms was presented and refined; and b) a presentation at the 2022-IADR annual meeting. The fourth stage consisted of two rounds of feedback to solicit experts' opinion about the terminology and group discussion to reach consensus. A Delphi-questionnaire was sent online to all experts to independently assess a) the appropriateness of the terms, and b) the accompanying definitions, and vote on whether they agreed with them. In a second round, each expert received an individualised questionnaire, which presented the expert's own responses from the first round and the panellists' overall response (% agreement/disagreement) to each term. It was decided that 70% or higher agreement among experts on the terms and definitions would represent consensus. RESULTS The study led to the identification of an initial set of 43 terms. The list of initial terms was refined to a core set of 37 terms. Initially, 34 experts took part in the consensus process about terms and definitions. From them, 27 experts completed the first rounds of consultations, and 15 the final round of consultations. All terms and definitions were confirmed via online voting (i.e., achieving above the agreed 70% threshold), which indicate their agreed recommendation for use in e-oral health research, dental public health, and clinical practice. CONCLUSION This is the first study in oral health organised to achieve consensus in e-oral health terminology. This terminology is presented as a resource for interested parties. These terms were also conceptualised to suit with the new healthcare ecosystem and the place of e-oral health within it. The universal use of this terminology to label interventions in future research will increase the homogeneity of future studies including systematic reviews.
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Affiliation(s)
- Rodrigo J Mariño
- Center for Research in Epidemiology, Economics and Oral Public Health (CIEESPO), Faculty of Dentistry, Universidad de La Frontera, Temuco, Chile
- Melbourne Dental School, University of Melbourne, Melbourne, Australia
| | - Sergio E Uribe
- Faculty of Dentistry, University of Valparaiso, Valparaiso, Chile
- Department of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia
- Baltic Biomaterials Centre of Excellence, Headquarters, Riga Technical University, Riga, Latvia
| | - Rebecca Chen
- Westmead Applied Research Centre, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia
| | - Falk Schwendicke
- Clinic for Conservative Dentistry and Periodontology, LMU University Hospital, LMU, Munich, Germany
| | - Nicolas Giraudeau
- Division CEPEL Organization CNRS, University of Montpellier, 163 rue Auguste Broussonnet, Montpellier, 34090, France
| | - Janneke F M Scheerman
- Department of Oral Healthcare; Health, Sports and Welfare, InHolland University of Applied Sciences, Gustav Mahlerlaan 3004, Amsterdam, Noord-Holland, 1081LA, The Netherlands.
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Mafla AC, Herrera-López M, Gallardo-Pino C, Schwendicke F. Psychometric Testing of HeLD-14 in a Colombian Geriatric Population. Int J Dent 2024; 2024:5570671. [PMID: 38357580 PMCID: PMC10866630 DOI: 10.1155/2024/5570671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 01/07/2024] [Accepted: 01/18/2024] [Indexed: 02/16/2024] Open
Abstract
Introduction The objective of this study was to test the validity and reliability of the Colombian version of the Health Literacy in Dentistry (HeLD-14) in older adults. Materials and Methods A translation and validation study of HeLD-14 was conducted on 384 non-institutionalized older adults attending the Dental Clinic at Universidad Cooperativa from Pasto, Colombia. A cross-cultural adaptation of a multidimensional HeLD-14 was completed, and the psychometric properties of this scale were evaluated through a cross-validation method using an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA). Internal consistency was measured with Cronbach's alpha (α) and Omega's McDonald (ɷ). The statistical significance was set at P < 0.05. Results The EFA demonstrated that a single-factor structure with 11 items explained a cumulative 59.86% of the overall variance. The CFA confirmed that goodness of fit indices of this questionnaire had optimal adequateness (χ2S-B = 109.047; χ2S-B/(44) = 2.478, P=0.001; non-normed fit index = 0.901; comparative fit index = 0.908; root mean square error of approximation = 0.079 (90% CI (0.075, 0.083)); standardized root mean residual = 0.080). The coefficients indicated a high internal consistency for the total scale (α = 0.94; ɷ = 0.96). Conclusion The developed adaptation of HeLD-14 for the Colombian population, HeLD-Col, is a unidimensional, reliable, and valid instrument to assess oral health literacy in older adults in Colombia.
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Affiliation(s)
- Ana Cristina Mafla
- Escuela Internacional de Doctorado, Universidad Rey Juan Carlos, Madrid, Spain
- School of Dentistry, Universidad Cooperativa de Colombia, Pasto, Colombia
| | | | - Carmen Gallardo-Pino
- Departamento de Especialidades Médicas y Salud Pública, Facultad Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité Universitätsmedizin Berlin, Berlin, Germany
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12
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Büttner M, Leser U, Schneider L, Schwendicke F. Natural Language Processing: Chances and Challenges in Dentistry. J Dent 2024; 141:104796. [PMID: 38072335 DOI: 10.1016/j.jdent.2023.104796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023] Open
Abstract
INTRODUCTION Natural language processing (NLP) is an intersection between Computer Science and Linguistic which aims to enable machines to process and understand human language. We here summarized applications and limitations of NLP in dentistry. DATA AND SOURCES Narrative review. FINDINGS NLP has evolved increasingly fast. For the dental domain, relevant NLP applications are text classification (e.g., symptom classification) and natural language generation and understanding (e.g., clinical chatbots assisting professionals in office work and patient communication). Analyzing large quantities of text will allow understanding diseases and their trajectories and support a more precise and personalized care. Speech recognition systems may serve as virtual assistants and facilitate automated documentation. However, to date, NLP has rarely been applied in dentistry. Existing research focuses mainly on rule-based solutions for narrow tasks. Technologies such as Recurrent Neural Networks and Transformers have been shown to surpass the language processing capabilities of such rule-based solutions in many fields, but are data-hungry (i.e., rely on large amounts of training data), which limits their application in the dental domain at present. Technologies such as federated or transfer learning or data sharing concepts may allow to overcome this limitation, while challenges in terms of explainability, reproducibility, generalizability and evaluation of NLP in dentistry remain to be resolved for enabling approval of such technologies in medical devices and services. CONCLUSIONS NLP will become a cornerstone of a number of applications in dentistry. The community is called to action to improve the current limitations and foster reliable, high-quality dental NLP. CLINICAL SIGNIFICANCE NLP for text classification (e.g., dental symptom classification) and language generation and understanding (e.g., clinical chatbots, speech recognition) will support administrative tasks in dentistry, provide deeper insights for clinicians and support research and education.
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Affiliation(s)
- Martha Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany.
| | - Ulf Leser
- Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lisa Schneider
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany
| | - Falk Schwendicke
- Clinic for Operative, Preventive and Pediatric Dentistry and Periodontology, Ludwig-Maximilians-University, Munich, Germany
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Geiken A, Banz L, Kock M, Schwendicke F, Graetz C. Does information about MIH on dental homepages in Germany offer high quality? A systematic search and analysis. Eur Arch Paediatr Dent 2024; 25:127-135. [PMID: 38300412 PMCID: PMC10942881 DOI: 10.1007/s40368-023-00857-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024]
Abstract
PURPOSE The internet is increasingly used to seek health information. A dental condition of increasing concern and public interest is molar incisor hypomineralisation (MIH), why we evaluated the information quality of German dentists 'websites on the topic of MIH. METHODS A systematic search was performed by two independent investigators using three search engines. The information content of websites on MIH and technical, functional aspects, overall quality, and risk of bias were assessed using validated instruments (LIDA, DISCERN). Practice-related characteristics (practice type, specialization, setting, number and mean age of dentists) were recorded, and associations of these characteristics with websites' overall quality were explored using multivariable linear regression modelling. RESULTS 70 sites were included. 52% were multipractices in urban areas (49%). The most common age group was middle-aged individuals (41-50 years). The average number of dentists/practice was 2.5. The majority met more than 50% of the DISCERN and LIDA criteria (90%, 91%). The MIH definition was frequently used (67%), MIH symptoms were described (64%), and 58% mentioned therapies. The prevalence of MIH was mentioned less frequently (48%). MIH example photographs were rarely shown (14%). In multivariable analysis, most practice-related factors were not significant for overall site quality. Only chain practices had slightly higher quality in this regard (2.2; 95% CI of 0.3-4.1). CONCLUSIONS MIH is mentioned on a large proportion of dentists' websites. Overall technical, functional, and generic quality was high. Risk of bias is limited. While most websites provided a basic definition of MIH and its symptoms, important information for patients was missing.
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Affiliation(s)
- A Geiken
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, Arnold-Heller-Str. 3, Haus B, 24105, Kiel, Germany.
| | - L Banz
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, Arnold-Heller-Str. 3, Haus B, 24105, Kiel, Germany
| | - M Kock
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, Arnold-Heller-Str. 3, Haus B, 24105, Kiel, Germany
| | - F Schwendicke
- Clinic for Conservative Dentistry and Periodontology, University Hospital of Ludwig-Maximilians-University Munich, Goethestr. 70, 80336, Munich, Germany
| | - C Graetz
- Clinic of Conservative Dentistry and Periodontology, University of Kiel, Arnold-Heller-Str. 3, Haus B, 24105, Kiel, Germany
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14
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Rokhshad R, Zhang P, Mohammad-Rahimi H, Shobeiri P, Schwendicke F. Current Applications of Artificial Intelligence for Pediatric Dentistry: A Systematic Review and Meta-Analysis. Pediatr Dent 2024; 46:27-35. [PMID: 38449036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
Purpose: To systematically evaluate artificial intelligence applications for diagnostic and treatment planning possibilities in pediatric dentistry. Methods: PubMed®, EMBASE®, Scopus, Web of Science™, IEEE, medRxiv, arXiv, and Google Scholar were searched using specific search queries. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist was used to assess the risk of bias assessment of the included studies. Results: Based on the initial screening, 33 eligible studies were included (among 3,542). Eleven studies appeared to have low bias risk across all QUADAS-2 domains. Most applications focused on early childhood caries diagnosis and prediction, tooth identification, oral health evaluation, and supernumerary tooth identification. Six studies evaluated AI tools for mesiodens or supernumerary tooth identification on radigraphs, four for primary tooth identification and/or numbering, seven studies to detect caries on radiographs, and 12 to predict early childhood caries. For these four tasks, the reported accuracy of AI varied from 60 percent to 99 percent, sensitivity was from 20 percent to 100 percent, specificity was from 49 percent to 100 percent, F1-score was from 60 percent to 97 percent, and the area-under-the-curve varied from 87 percent to 100 percent. Conclusions: The overall body of evidence regarding artificial intelligence applications in pediatric dentistry does not allow for firm conclusions. For a wide range of applications, AI shows promising accuracy. Future studies should focus on a comparison of AI against the standard of care and employ a set of standardized outcomes and metrics to allow comparison across studies.
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Affiliation(s)
- Rata Rokhshad
- Researchers, Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Ping Zhang
- professor, Department of Pediatric Dentistry, University of Alabama at Birmingham, Birmingham, Ala., USA
| | - Hossein Mohammad-Rahimi
- Researchers, Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Parnian Shobeiri
- Researcher, School of Medicine, Tehran University of Medical Science, Tehran, Iran
| | - Falk Schwendicke
- professor, Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, and a professor, Conservative dentistry and periodontology, Ludwig-Maximilians-Universit??t München, Munich, Germany
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15
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Rokhshad R, Mohammad-Rahimi H, Price JB, Shoorgashti R, Abbasiparashkouh Z, Esmaeili M, Sarfaraz B, Rokhshad A, Motamedian SR, Soltani P, Schwendicke F. Artificial intelligence for classification and detection of oral mucosa lesions on photographs: a systematic review and meta-analysis. Clin Oral Investig 2024; 28:88. [PMID: 38217733 DOI: 10.1007/s00784-023-05475-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 12/21/2023] [Indexed: 01/15/2024]
Abstract
OBJECTIVE This study aimed to review and synthesize studies using artificial intelligence (AI) for classifying, detecting, or segmenting oral mucosal lesions on photographs. MATERIALS AND METHOD Inclusion criteria were (1) studies employing AI to (2) classify, detect, or segment oral mucosa lesions, (3) on oral photographs of human subjects. Included studies were assessed for risk of bias using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). A PubMed, Scopus, Embase, Web of Science, IEEE, arXiv, medRxiv, and grey literature (Google Scholar) search was conducted until June 2023, without language limitation. RESULTS After initial searching, 36 eligible studies (from 8734 identified records) were included. Based on QUADAS-2, only 7% of studies were at low risk of bias for all domains. Studies employed different AI models and reported a wide range of outcomes and metrics. The accuracy of AI for detecting oral mucosal lesions ranged from 74 to 100%, while that for clinicians un-aided by AI ranged from 61 to 98%. Pooled diagnostic odds ratio for studies which evaluated AI for diagnosing or discriminating potentially malignant lesions was 155 (95% confidence interval 23-1019), while that for cancerous lesions was 114 (59-221). CONCLUSIONS AI may assist in oral mucosa lesion screening while the expected accuracy gains or further health benefits remain unclear so far. CLINICAL RELEVANCE Artificial intelligence assists oral mucosa lesion screening and may foster more targeted testing and referral in the hands of non-specialist providers, for example. So far, it remains unclear if accuracy gains compared with specialized can be realized.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Jeffery B Price
- Department of Oncology and Diagnostic Sciences, University of Maryland, School of Dentistry, Baltimore, Maryland 650 W Baltimore St, Baltimore, MD, 21201, USA
| | - Reyhaneh Shoorgashti
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | | | - Mahdieh Esmaeili
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Bita Sarfaraz
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran
| | - Arad Rokhshad
- Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, 9Th Neyestan, Pasdaran, Tehran, Iran
| | - Saeed Reza Motamedian
- Dentofacial Deformities Research Center, Research Institute of Dental Sciences & Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Daneshjoo Blvd, Evin, Shahid Chamran Highway, Tehran, Postal Code: 1983963113, Iran.
| | - Parisa Soltani
- Department of Oral and Maxillofacial Radiology, Dental Implants Research Center, Dental Research Institute, School of Dentistry, Isfahan University of Medical Sciences, Salamat Blv, Isfahan Dental School, Isfahan, Iran
- Department of Neurosciences, Reproductive and Odontostomatological Sciences, University of Naples Federico II, Nepales, Italy
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI On Health, Berlin, Germany
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Charitépl. 1, 10117, Berlin, Germany
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16
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Mafla AC, Herrera-López M, Dorado-Pantoja GT, López-Ruano KJ, Saa-Valentierra L, Gallardo-Pino C, Schwendicke F. Oral Health Literacy and Tooth Loss and Replacement in Older Adults at a University Dental Clinic in Colombia. Health Lit Res Pract 2024; 8:e21-e28. [PMID: 38329842 PMCID: PMC10849777 DOI: 10.3928/24748307-20240121-01] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 08/14/2023] [Indexed: 02/10/2024] Open
Abstract
BACKGROUND Oral health literacy (OHL) is the ability of individuals to obtain, process, and understand oral health information and services, allowing them to make appropriate oral health decisions. The association between OHL and tooth loss and replacement have not been well understood. OBJECTIVES We aimed to determine the association between OHL and tooth loss and replacement in a Colombia population. METHODS A cross-sectional study of 384 older adults age 65 to 89 years from Pasto, Colombia was carried out. The number of lost and replaced teeth was assessed intraorally; sociodemographic and prosthetic characteristics were collected, and the Health Literacy in Dentistry questionnaire was used to evaluate OHL. Generalized linear models were estimated to assess associations between independent variables (including OHL) and the number of lost and replaced teeth. KEY RESULTS There were 224 (58.3%) men and 160 (41.7%) women. The mean (standard deviation [SD]) number of lost and replaced teeth was 27.78 (4.03) and 12.53 (9.89), respectively. One hundred fifty five (40.4%) individuals had full removable dental protheses, 122 (31.8%) partial removable dental protheses, 68 (17.7%) fixed prosthetics, and 36 (9.4%) dental implants. OHL was 33.29 (6.59) and significantly positively associated with the number of replaced teeth (β = 0.65, 95% confidence interval [CI]: 0.52-0.78, p < .001), but not with lost teeth. CONCLUSIONS OHL may foster individuals' capabilities to replace lost teeth, although we did not find it associated with reduced tooth loss, likely as tooth loss was highly common in this older population. [HLRP: Health Literacy Research and Practice. 2024;8(1):e21-e28.].
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Affiliation(s)
- Ana Cristina Mafla
- Address correspondence to Ana Cristina Mafla, DDS, MSPH, Facultad de Odontología, Universidad Cooperativa de Colombia, Calle 18 No. 45 – 150, Pasto, Colombia;
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17
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Arsiwala-Scheppach LT, Castner NJ, Rohrer C, Mertens S, Kasneci E, Cejudo Grano de Oro JE, Schwendicke F. Impact of artificial intelligence on dentists' gaze during caries detection: A randomized controlled trial. J Dent 2024; 140:104793. [PMID: 38016620 DOI: 10.1016/j.jdent.2023.104793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/15/2023] [Accepted: 11/24/2023] [Indexed: 11/30/2023] Open
Abstract
OBJECTIVES We aimed to understand how artificial intelligence (AI) influences dentists by comparing their gaze behavior when using versus not using an AI software to detect primary proximal carious lesions on bitewing radiographs. METHODS 22 dentists assessed a median of 18 bitewing images resulting in 170 datasets from dentists without AI and 179 datasets from dentists with AI, after excluding data with poor gaze recording quality. We compared time to first fixation, fixation count, average fixation duration, and fixation frequency between both trial groups. Analyses were performed for the entire image and stratified by (1) presence of carious lesions and/or restorations and (2) lesion depth (E1/2: outer/inner enamel; D1-3 outer-inner third of dentin). We also compared the transitional pattern of the dentists' gaze between the trial groups. RESULTS Median time to first fixation was shorter in all groups of teeth for dentists with AI versus without AI, although p>0.05. Dentists with AI had more fixations (median=68, IQR=31, 116) on teeth with restorations compared to dentists without AI (median=47, IQR=19, 100), p = 0.01. In turn, average fixation duration was longer on teeth with caries for the dentists with AI than those without AI; although p>0.05. The visual search strategy employed by dentists with AI was less systematic with a lower proportion of lateral tooth-wise transitions compared to dentists without AI. CONCLUSIONS Dentists with AI exhibited more efficient viewing behavior compared to dentists without AI, e.g., lesser time taken to notice caries and/or restorations, more fixations on teeth with restorations, and fixating for shorter durations on teeth without carious lesions and/or restorations. CLINICAL SIGNIFICANCE Analysis of dentists' gaze patterns while using AI-generated annotations of carious lesions demonstrates how AI influences their data extraction methods for dental images. Such insights can be exploited to improve, and even customize, AI-based diagnostic tools, thus reducing the dentists' extraneous attentional processing and allowing for more thorough examination of other image areas.
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Affiliation(s)
- Lubaina T Arsiwala-Scheppach
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Aßmannshauser Straße 4-6, 14197, Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Germany.
| | | | - Csaba Rohrer
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Sarah Mertens
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Enkelejda Kasneci
- Human-Centered Technologies for Learning, Technical University Munich, Germany
| | - Jose Eduardo Cejudo Grano de Oro
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Aßmannshauser Straße 4-6, 14197, Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Germany
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18
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Mafla AC, Orozco-Tovar AE, Ortiz-Gómez F, Ortiz-Pizán ÁJ, González-Ruano AV, Schwendicke F. Association between psychological factors and molar-incisor hypomineralization: A cross-sectional study. Int J Paediatr Dent 2023. [PMID: 38013224 DOI: 10.1111/ipd.13142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/03/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
BACKGROUND Molar-incisor hypomineralization (MIH) is a developmental enamel defect characterized by opacities from white to brownish color. A suspected multifactorial etiology has been suggested, whereas psychological factors during pregnancy have only been limitedly analyzed. AIM We assessed the association between stress, depression, and anxiety in pregnancy and the presence of MIH in children at a later age. DESIGN Using a cross-sectional Web-based questionnaire, we included 384 mothers who had children aged 6 and 12 years from Pasto, Colombia. Data were collected between October 2021 and March 2022. Sociodemographic variables; maternal and child factors related to prenatal, natal, or postnatal problems; and psychological factors such as stress and symptoms of anxiety and depression in pregnancy were inquired. Utilizing photographs depicting MIH lesions, mothers assessed their child's MIH status. A directed acyclic graph (DAG) analysis was performed to create causal assumptions, and logistic regression models were estimated to evaluate these assumptions. p-value was set at p < .05. RESULTS The prevalence of MIH was 33.3%; 12.8% of the participants exhibited hypomineralization in both molars and incisors. DAG analysis and logistic regression models determined that MIH (present or not) was associated with symptoms of maternal depression (ORadj = 3.26, 95% CI: 1.92-5.52, p < .001), and MIH (both molars and incisors) was associated with symptoms of maternal anxiety (ORadj = 3.49, 95% CI: 1.80-6.76, p < .001). CONCLUSION Psychological factors, among others, were significantly associated with the presence of MIH.
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Affiliation(s)
- Ana Cristina Mafla
- School of Dentistry, Universidad Cooperativa de Colombia, Pasto, Colombia
- Escuela Internacional de Doctorado, Universidad Rey Juan Carlos, Madrid, Spain
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité Universitätsmedizin Berlin, Berlin, Germany
| | | | | | | | | | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité Universitätsmedizin Berlin, Berlin, Germany
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Sardana D, Schwendicke F, Kosan E, Tüfekçi E. White spot lesions in orthodontics: consensus statements for prevention and management. Angle Orthod 2023; 93:621-628. [PMID: 37548264 PMCID: PMC10633804 DOI: 10.2319/062523-440.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Accepted: 06/01/2023] [Indexed: 08/08/2023] Open
Abstract
OBJECTIVE To establish consensus recommendations for clinicians to manage white spot lesions (WSLs) during orthodontic treatment. MATERIALS AND METHODS Three task force members reviewed the literature to identify best practices for minimizing WSLs during orthodontic treatment. Each draft statement was read to the task force members by a facilitator, followed by voting, accepting, or editing if necessary. The statements were then sent electronically by an independent third party (Magellan Medical Technology Consultants Inc, Minneapolis, Minn) to a previously formed content validation panel consisting of 20 independent private practitioners and clinical academicians for validation. RESULTS Twenty-one statements were developed and sent for content validation. While 19 statements achieved a content validation index (CVI) of 0.78, two items did not. These items were edited by the task force members based on qualitative feedback from content validation participants. Each of these revised statements did achieve a CVI of 0.78 on second evaluation from the content validation panelists and therefore were included in this document. CONCLUSION To reduce the risk of WSLs, it is essential to implement individualized caries management measures based on a comprehensive assessment of the patient's oral and systemic health. Effective at-home and professional mechanical and chemical plaque control should be implemented for high-risk orthodontic patients. Fluoride to support prevention and materials such as orthodontic sealants should also be used to provide a physical barrier around the brackets in high-risk patients. By following these guidelines, orthodontic professionals can help promote oral health and minimize the need for restorative treatment.
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Affiliation(s)
| | | | | | - Eser Tüfekçi
- Corresponding author: Eser Tüfekçi, Department of Orthodontics, School of Dentistry, Virginia Commonwealth University, PO Box 980566, Richmond, VA 23298-0566, USA (e-mail: )
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Schwendicke F, Badakhsh P, Marques MG, Demarchi KM, Brant ARR, Moreira CL, Ribeiro APD, Leal SC, Hilgert LA. Subjective versus objective, polymer bur-based selective carious tissue removal: 2-year randomized clinical trial. J Dent 2023; 138:104728. [PMID: 37783372 DOI: 10.1016/j.jdent.2023.104728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 09/25/2023] [Accepted: 09/26/2023] [Indexed: 10/04/2023] Open
Abstract
OBJECTIVES We aimed to compare subjective (S) selective carious tissue removal using hand instruments versus objective (O) removal using a self-limiting polymer bur in a single-blind cluster-randomized controlled superiority trial. METHODS 115 children (aged 7-8 years) with ≥1 vital primary molar with a deep dentin lesion (>1/2 dentin depth) were included and randomized (60 S/55 O); all eligible molars in a child were treated identically (91 S/86 O). Cavities were prepared and carious tissue on pulpal walls selectively removed using hand instruments (S) or a self-limiting polymer bur (Polybur P1, Komet), followed by restoration using a glass hybrid material (Equia Forte, GC). Treatment time and satisfaction data have been reported in a 1-year-interim report. We here report on 2 year survival (tooth retained with or without further retreatments being needed, or tooth exfoliated), analyzed using multi-level Cox-regression analysis, as well as success (ART criteria 0/1, no pulpal complications, no re-intervention needed, or tooth extraction). RESULTS 71 restorations in S and 65 in O were examined after a mean (SD, range) of 22 (11; 3-31) months, of which 50 S and 48 O restorations were successful and 70 S and 65 O survived. The majority of failures were restorative, not pulpal, and distribution of ART codes was not significant different between groups. Risk of failure was not significantly associated with the removal protocol (HR; 95 % CI: 0.95; 0.51-1.78), and also not age, sex or dental arch, while single surfaced restorations showed significantly lower hazard (0.14; 0.06-0.37). CONCLUSION There was no significant difference in success or survival between objective and subjective carious tissue removal. CLINICAL SIGNIFICANCE In primary teeth, subjective selective excavation had no disadvantage compared with objective excavation, which required a separate instrument (polymer-based bur) for carious tissue removal. Polymer-based burs may be particularly useful when standardized excavation is needed.
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Affiliation(s)
- Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité- Universitätsmedizin, Aßmannshauser Str. 4-6, Berlin 14197, Germany.
| | - Puya Badakhsh
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité- Universitätsmedizin, Aßmannshauser Str. 4-6, Berlin 14197, Germany
| | - Marta Gomes Marques
- School of Health Sciences, Department of Dentistry, University of Brasília, Brasília, Brazil
| | | | | | - Cláudia Lúcia Moreira
- School of Health Sciences, Department of Dentistry, University of Brasília, Brasília, Brazil
| | - Ana Paula Dias Ribeiro
- School of Health Sciences, Department of Dentistry, University of Brasília, Brasília, Brazil; Department of Restorative Dental Sciences, College of Dentistry, University of Florida, Gainesville, FL, United States
| | - Soraya Coelho Leal
- School of Health Sciences, Department of Dentistry, University of Brasília, Brasília, Brazil
| | - Leandro Augusto Hilgert
- School of Health Sciences, Department of Dentistry, University of Brasília, Brasília, Brazil
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21
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Ciani O, Manyara AM, Davies P, Stewart D, Weir CJ, Young AE, Blazeby J, Butcher NJ, Bujkiewicz S, Chan AW, Dawoud D, Offringa M, Ouwens M, Hróbjartssson A, Amstutz A, Bertolaccini L, Bruno VD, Devane D, Faria CD, Gilbert PB, Harris R, Lassere M, Marinelli L, Markham S, Powers JH, Rezaei Y, Richert L, Schwendicke F, Tereshchenko LG, Thoma A, Turan A, Worrall A, Christensen R, Collins GS, Ross JS, Taylor RS. A framework for the definition and interpretation of the use of surrogate endpoints in interventional trials. EClinicalMedicine 2023; 65:102283. [PMID: 37877001 PMCID: PMC10590868 DOI: 10.1016/j.eclinm.2023.102283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/03/2023] [Accepted: 10/03/2023] [Indexed: 10/26/2023] Open
Abstract
Background Interventional trials that evaluate treatment effects using surrogate endpoints have become increasingly common. This paper describes four linked empirical studies and the development of a framework for defining, interpreting and reporting surrogate endpoints in trials. Methods As part of developing the CONSORT (Consolidated Standards of Reporting Trials) and SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) extensions for randomised trials reporting surrogate endpoints, we undertook a scoping review, e-Delphi study, consensus meeting, and a web survey to examine current definitions and stakeholder (including clinicians, trial investigators, patients and public partners, journal editors, and health technology experts) interpretations of surrogate endpoints as primary outcome measures in trials. Findings Current surrogate endpoint definitional frameworks are inconsistent and unclear. Surrogate endpoints are used in trials as a substitute of the treatment effects of an intervention on the target outcome(s) of ultimate interest, events measuring how patients feel, function, or survive. Traditionally the consideration of surrogate endpoints in trials has focused on biomarkers (e.g., HDL cholesterol, blood pressure, tumour response), especially in the medical product regulatory setting. Nevertheless, the concept of surrogacy in trials is potentially broader. Intermediate outcomes that include a measure of function or symptoms (e.g., angina frequency, exercise tolerance) can also be used as substitute for target outcomes (e.g., all-cause mortality)-thereby acting as surrogate endpoints. However, we found a lack of consensus among stakeholders on accepting and interpreting intermediate outcomes in trials as surrogate endpoints or target outcomes. In our assessment, patients and health technology assessment experts appeared more likely to consider intermediate outcomes to be surrogate endpoints than clinicians and regulators. Interpretation There is an urgent need for better understanding and reporting on the use of surrogate endpoints, especially in the setting of interventional trials. We provide a framework for the definition of surrogate endpoints (biomarkers and intermediate outcomes) and target outcomes in trials to improve future reporting and aid stakeholders' interpretation and use of trial surrogate endpoint evidence. Funding SPIRIT-SURROGATE/CONSORT-SURROGATE project is Medical Research Council Better Research Better Health (MR/V038400/1) funded.
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Affiliation(s)
- Oriana Ciani
- Centre for Research on Health and Social Care Management, SDA Bocconi School of Management, Milan, Italy
| | - Anthony M. Manyara
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Philippa Davies
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | | | - Christopher J. Weir
- Edinburgh Clinical Trials Unit, Usher Institute, University of Edinburgh, Edinburgh, UK
| | | | - Jane Blazeby
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Bristol NIHR Biomedical Research Centre, Bristol, UK
- University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, UK
| | - Nancy J. Butcher
- Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Sylwia Bujkiewicz
- Biostatistics Research Group, Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - An-Wen Chan
- Women's College Research Institute, Toronto, Canada
- Department of Medicine, University of Toronto, Toronto, Canada
| | - Dalia Dawoud
- Science, Evidence and Analytics Directorate, Science Policy and Research Programme, National Institute for Health and Care Excellence, London, UK
| | - Martin Offringa
- Child Health Evaluative Sciences, The Hospital for Sick Children Research Institute, Toronto, Canada
- Department of Paediatrics, University of Toronto, Toronto, Canada
| | | | - Asbjørn Hróbjartssson
- Centre for Evidence-Based Medicine Odense (CEBMO) and Cochrane Denmark, Department of Clinical Research, University of Southern Denmark, Odense, Denmark
- Open Patient Data Explorative Network (OPEN), Odense University Hospital, Odense, Denmark
| | - Alain Amstutz
- CLEAR Methods Center, Division of Clinical Epidemiology, Department of Clinical Research, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Luca Bertolaccini
- Department of Thoracic Surgery, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Vito Domenico Bruno
- Department of Minimally Invasive Cardiac Surgery, IRCCS Galeazzi – Sant’Ambrogio Hospital, Milan, Italy
| | - Declan Devane
- University of Galway, Galway, Ireland
- Health Research Board-Trials Methodology Research Network, University of Galway, Galway, Ireland
| | - Christina D.C.M. Faria
- Department of Physical Therapy, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | | | - Ray Harris
- Patient and Public Involvement Partner, UK
| | - Marissa Lassere
- St George Hospital and School of Population Health, The University of New South Wales, Sydney, Australia
| | - Lucio Marinelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genova, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Sarah Markham
- Department of Biostatistics, King's College London, London, UK
| | - John H. Powers
- George Washington University School of Medicine, Washington, USA
| | - Yousef Rezaei
- Heart Valve Disease Research Center, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran
- Ardabil University of Medical Sciences, Ardabil, Iran
- Behyan Clinic, Pardis New Town, Tehran, Iran
| | - Laura Richert
- University Bordeaux, INSERM, Institut Bergonié, CHU Bordeaux, BPH U1219, CIC-EC 1401, RECaP and Euclid/F-CRIN, Bordeaux, France
| | | | - Larisa G. Tereshchenko
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | - Alparslan Turan
- Department of Outcomes Research, Anesthesiology Institute, Cleveland Clinic, OH, USA
| | | | - Robin Christensen
- Section for Biostatistics and Evidence-Based Research, The Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen & Research Unit of Rheumatology, Department of Clinical Research, University of Southern Denmark, Odense University Hospital, Odense, Denmark
| | - Gary S. Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Joseph S. Ross
- Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA
- Section of General Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rod S. Taylor
- MRC/CSO Social and Public Health Sciences Unit, School of Health and Wellbeing, University of Glasgow, Glasgow, UK
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22
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Messer-Hannemann P, Böttcher H, Henning S, Schwendicke F, Effenberger S. Concept of a Novel Glass Ionomer Restorative Material with Improved Mechanical Properties. J Funct Biomater 2023; 14:534. [PMID: 37998103 PMCID: PMC10672254 DOI: 10.3390/jfb14110534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 10/12/2023] [Accepted: 10/18/2023] [Indexed: 11/25/2023] Open
Abstract
The objective of this study was to transfer the concept of ductile particle reinforcement to restorative dentistry and to introduce an innovative glass ionomer material that is based on the dispersion of PEG-PU micelles. It was hypothesized that reinforcing a conventional glass ionomer in this way increases the flexural strength and fracture toughness of the material. Flexural strength and fracture toughness tests were performed with the novel reinforced and a control glass ionomer material (DMG, Hamburg, Germany) to investigate the influence of the dispersed micelles on the mechanical performance. Transmission electron microscopy was used to identify the dispersed micelles. Fracture toughness and flexural strength were measured in a 3-point-bending setup using a universal testing machine. Before performing both tests, the specimens were stored in water at 37 °C for 23 h. The fracture toughness (MPa∙m0.5) of the novel glass ionomer material (median: 0.92, IQR: 0.89-0.94) was significantly higher than that of the control material (0.77, 0.75-0.86, p = 0.0078). Significant differences were also found in the flexural strength (MPa) between the reinforced (49.7, 45.2-57.8) and control material (41.8, 40.6-43.5, p = 0.0011). Reinforcing a conventional glass ionomer with PEG-PU micelles improved the mechanical properties and may expand clinical applicability of this material class in restorative dentistry.
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Affiliation(s)
| | - Henrik Böttcher
- DMG Dental-Material Gesellschaft mbH, 22547 Hamburg, Germany
| | - Sven Henning
- Fraunhofer Institute for Microstructure of Materials and Systems IMWS, 06120 Halle (Saale), Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, 14197 Berlin, Germany
| | - Susanne Effenberger
- DMG Dental-Material Gesellschaft mbH, 22547 Hamburg, Germany
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, 14197 Berlin, Germany
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23
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Schwendicke F, Bombeck L. Cost-effectiveness of school-based caries screening using transillumination. J Dent 2023; 137:104635. [PMID: 37541420 DOI: 10.1016/j.jdent.2023.104635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 07/23/2023] [Accepted: 07/24/2023] [Indexed: 08/06/2023] Open
Abstract
OBJECTIVES School-based screening for caries lesions usually only employs visual-tactile detection means (standard of care). Near-infrared-light-transillumination (NILT) could be used to support school-based screening and to identify early proximal caries, facilitating referral and appropriate non- or micro-invasive management in dental practice. METHOD We assessed the cost-effectiveness of NILT for school-based caries screening. A German mixed-payers' perspective was adopted. A Markov model was used to simulate the consequences of true and false positive and negative detections and the subsequent decisions over the lifetime of initially 12 years old patients. Our health outcome was tooth retention in years. Costs were measured in Euro 2020. Monte-Carlo-microsimulations, univariate and probabilistic sensitivity analyses were conducted. The incremental cost-effectiveness ratio (ICER) and the cost-effectiveness-acceptability at different willingness-to-pay-thresholds were quantified. RESULTS NILT was minimally more effective (tooth retention for a mean (2.5-97.5%) 56 (53-59) years) and minimally less costly (515 (416-616) Euro) than standard of care (56 (50-59) years; 526 (427-628 Euro)). The ICER was -503 Euro/year, i.e. school-based caries screening using NILT saved money at higher effectiveness in the modelled population. The cost-effectiveness of NILT increased for payers with a willingness-to-pay for additional tooth retention time. The biggest driver of costs were (avoided) tooth replacements later in life. CONCLUSIONS NILT-based screening is likely to yield limited effectiveness gains and cost savings in the modelled populations. In countries where regular practice-based screening of children is less common than in Germany, the cost-effectiveness of NILT for school-based caries screening is likely higher. CLINICAL SIGNIFICANCE NILT-based caries screening in German schools is unlikely to be cost-effective. In countries with different utilization patterns or generally higher caries prevalence and risk, this may differ.
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Affiliation(s)
- Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany.
| | - Lisa Bombeck
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany
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24
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Danek S, Achelrod D, Wichmann O, Schwendicke F. The Role of Vaccination Centers in a National Mass Immunization Campaign-Policymaker Insights from the German COVID-19 Pandemic Vaccine Roll-Out. Vaccines (Basel) 2023; 11:1552. [PMID: 37896955 PMCID: PMC10611148 DOI: 10.3390/vaccines11101552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/29/2023] Open
Abstract
During the COVID-19 vaccination campaign, Germany, like other high-income countries, introduced mass vaccination centers for administering vaccinations. This qualitative study aimed to examine the role that these novel, temporary government healthcare structures played in a mass immunization roll-out and how they can be optimally deployed. In addition, learnings for general emergency preparedness were explored. A total of 27 high-level policymakers responsible for planning and implementing the COVID vaccination campaign at the national and state level in Germany were interviewed in May and June 2022. The semi-structured interviews were analyzed using thematic analysis. Interviewees indicated that mass vaccination structures played an essential role with respect to controllability, throughput, accessibility and openness in line with the key success criteria vaccination coverage, speed and accessibility. In contrast to the regular vaccination structures (private medical practices and occupational health services), public administration has direct authority over mass vaccination centers, allowing for reliable vaccine access prioritization and documentation. The deployment of vaccination centers should be guided by vaccine availability and demand, and vaccine requirements related to logistics, as well as local capacities, i.e., public-health-service strength and the physician density, to ensure effective, timely and equitable access. Improvements to the capacity use, scalability and flexibility of governmental vaccination structures are warranted for future pandemics.
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Affiliation(s)
- Stella Danek
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral Diagnostics, Digital Health and Health Services Research, Assmannshauser Straβe 4-6, 14197 Berlin, Germany;
| | | | - Ole Wichmann
- Immunization Unit, Robert Koch Institute, 13353 Berlin, Germany;
| | - Falk Schwendicke
- Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Oral Diagnostics, Digital Health and Health Services Research, Assmannshauser Straβe 4-6, 14197 Berlin, Germany;
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25
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Bruhnke M, Naumann M, Beuer F, Böse MWH, von Stein-Lausnitz M, Schwendicke F. Implant or tooth? - A cost-time analysis of managing "unrestorable" teeth ✰. J Dent 2023; 136:104646. [PMID: 37527727 DOI: 10.1016/j.jdent.2023.104646] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/03/2023] Open
Abstract
OBJECTIVES Retaining and restoring severely compromised teeth with subcrestal defect extensions or removing and replacing them using implant-supported crowns (ISC) remains controversial, and economic analyses comparing both strategies remain scarce. We performed a cost-time analysis, comparing the expenditures for retaining "unrestorable" teeth using forced orthodontic extrusion and restoration (FOE) versus extraction and ISC, in a clinical prospective cohort study. METHODS Forty-two patients (n = 21 per group) were enrolled from clinical routine at a university into this study. Direct medical and indirect costs (opportunity costs) were assessed for all relevant steps (initial care, active care, restorative care, supportive care) using the private payer's perspective in German healthcare based on a micro-costing approach and/or national fee items. Statistical comparison was performed with Mann-Whitney-U test. RESULTS Patients were followed up for at least one year after initial treatment (n = 40). The drop-out rate was 5% (n = 2). Total direct medical costs were higher for ISC (median: 3439.05€) than FOE (median: 1601.46€) with p<0.001. We observed a higher number of appointments (p = 0.002) for ISC (median: 14.5) in comparison to FOE (median: 12), while cumulatively, FOE patients spent more time in treatment (median: 402.5 min) in comparison to ISC (median: 250 min) with p<0.001, resulting in comparable opportunity costs for both treatment groups (FOE: 304.50€; ISC: 328.98€). CONCLUSIONS ISC generated higher costs than FOE. More in-depth and long-term exploration of cost-effectiveness is warranted. CLINICAL SIGNIFICANCE ISCs were associated with higher initial medical costs and required more appointments than the restoration of severely compromised teeth after FOE. Treatment time was higher for patients with FOE, resulting in similar opportunity costs for both treatment approaches. Future research needs to investigate long-term cost-effectiveness.
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Affiliation(s)
- Maria Bruhnke
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany.
| | - Michael Naumann
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Florian Beuer
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Mats Wernfried Heinrich Böse
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Manja von Stein-Lausnitz
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders - Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt - Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
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26
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Bruhnke M, Naumann M, Böse MWH, Beuer F, Schwendicke F. Health economic evaluation of forced orthodontic extrusion of extensively damaged teeth: up to 6-year results from a clinical study. Clin Oral Investig 2023; 27:5587-5594. [PMID: 37498335 PMCID: PMC10492751 DOI: 10.1007/s00784-023-05178-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
OBJECTIVES Clinical data on retaining extensively damaged teeth using forced orthodontic extrusion followed by restorative rehabilitation are scarce, and economic evaluations are basically absent. A health economic evaluation of this method was performed based on a clinical study. MATERIALS AND METHODS In a convenience sample of individuals recruited from routine care, extensively damaged teeth were orthodontically extruded prior to restoration. Patients were followed up for up to 6 years. The health outcome was tooth retention time. Direct medical, non-medical, and indirect initial and follow-up costs were estimated using the private payer's perspective in German healthcare. Association of initial direct medical treatment costs and cofounding variables was analyzed using generalized linear models. Success and survival were secondary outcomes. RESULTS A total of 35 teeth in 30 patients were followed over a mean ± SD of 49 ± 19 months. Five patients (14%) dropped out during that period. Median initial costs were 1941€ (range: 1284-4392€), median costs for follow-up appointments were 215€ (range: 0-5812€), and median total costs were 2284€ (range: 1453 to 7109€). Endodontic re-treatment and placement of a post had a significant impact on total costs. Three teeth had to be extracted and in three patients orthodontic relapse was observed. The survival and success rates were 91% and 83%, respectively. CONCLUSIONS Within the limitations of this clinical study, total treatment costs for orthodontic extrusion and subsequent restoration of extensively damaged teeth were considerable. Costs were by large generated initially; endodontic and post-endodontic therapies were main drivers. Costs for retreatments due to complications were limited, as only few complications arose. CLINICAL RELEVANCE The restoration of extensively damaged teeth after forced orthodontic extrusion comes with considerable initial treatment costs, but low follow-up costs. Overall and over the observational period and within German healthcare, costs are below those for tooth replacement using implant-supported crowns. TRIAL REGISTRATION ClinicalTrials.gov Identifier: DRK S00026697).
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Affiliation(s)
- Maria Bruhnke
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany.
| | - Michael Naumann
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Mats Wernfried Heinrich Böse
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Florian Beuer
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Aßmannshauser Straße 4-6, 14197, Berlin, Germany
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Czwikla J, Rothgang H, Schwendicke F, Hoffmann F. Dental care utilization among home care recipients, nursing home residents, and older adults not in need of long-term care: An observational study based on German insurance claims data. J Dent 2023; 136:104627. [PMID: 37473830 DOI: 10.1016/j.jdent.2023.104627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 07/22/2023] Open
Abstract
OBJECTIVES To describe and compare dental care utilization (DCU) among home care recipients, nursing home residents, and older adults not in need of long-term care (LTC). METHODS Using nationwide claims data of 8 German statutory health and LTC insurance funds, proportions of home care recipients (n = 68,137), nursing home residents (n = 21,167), and non-LTC dependents (n = 632,205) aged 65+ years with DCU in 2017 were determined and compared. Associations between DCU and individual characteristics and setting were investigated via multivariable logistic regression. The proportions of individuals with DCU one year before and after transition to (a) home care (n = 23,590) and (b) nursing home care (n = 6,583) were compared. RESULTS Proportions of home care recipients and nursing home residents with DCU were lower compared to non-LTC dependents (51.9, 53.1, and 73.2%, respectively). Adjusted odds ratios for DCU for home care recipients vs. non-LTC dependents ranged from 0.55 (LTC grades 1/2; 95% confidence interval 0.54-0.56) to 0.38 (LTC grades 4/5; 0.36-0.40). For nursing home residents vs. non-LTC dependents they ranged from 0.69 (3; 0.65-0.72) to 0.67 (4/5; 0.63-0.71). Women, older individuals, those with 0-1 diseases of the Elixhauser comorbidity index, dementia, and those from West Germany were also less likely to utilize dental care than their counterparts. Utilization decreased after transition to home care (60.0 vs. 55.6%) and increased after transition to nursing homes (46.1 vs. 53.5%). CONCLUSIONS Nursing home residents and especially home care recipients utilized dental care less frequently than older non-LTC dependents. Organizational barriers for dental care utilization and ways to remove them should be investigated. CLINICAL SIGNIFICANCE Dental care utilization among LTC dependents is low and should be improved in both the home care and nursing home setting.
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Affiliation(s)
- Jonas Czwikla
- Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; Department of Health, Long-term Care and Pensions, SOCIUM Research Center on Inequality and Social Policy, University of Bremen, Mary-Somerville-Straße 5, 28359 Bremen, Germany; High-Profile Area of Health Sciences, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany.
| | - Heinz Rothgang
- Department of Health, Long-term Care and Pensions, SOCIUM Research Center on Inequality and Social Policy, University of Bremen, Mary-Somerville-Straße 5, 28359 Bremen, Germany; High-Profile Area of Health Sciences, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health, Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Straße 4-6, 14197 Berlin, Germany
| | - Falk Hoffmann
- Department of Health Services Research, Carl von Ossietzky University of Oldenburg, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany
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Herbst SR, Pitchika V, Krois J, Krasowski A, Schwendicke F. Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study. J Clin Med 2023; 12:5464. [PMID: 37685531 PMCID: PMC10488275 DOI: 10.3390/jcm12175464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
(1) Background: We aimed to identify factors associated with the presence of apical lesions (AL) in panoramic radiographs and to evaluate the predictive value of the identified factors. (2) Methodology: Panoramic radiographs from 1071 patients (age: 11-93 a, mean: 50.6 a ± 19.7 a) with 27,532 teeth were included. Each radiograph was independently assessed by five experienced dentists for AL. A range of shallow machine learning algorithms (logistic regression, k-nearest neighbor, decision tree, random forest, support vector machine, adaptive and gradient boosting) were employed to identify factors at both the patient and tooth level associated with AL and to predict AL. (3) Results: AL were detected in 522 patients (48.7%) and 1133 teeth (4.1%), whereas males showed a significantly higher prevalence than females (52.5%/44.8%; p < 0.05). Logistic regression found that an existing root canal treatment was the most important risk factor (adjusted Odds Ratio 16.89; 95% CI: 13.98-20.41), followed by the tooth type 'molar' (2.54; 2.1-3.08) and the restoration with a crown (2.1; 1.67-2.63). Associations between factors and AL were stronger and accuracy higher when using fewer complex models like decision tree (F1 score: 0.9 (0.89-0.9)). (4) Conclusions: The presence of AL was higher in root-canal treated teeth, those with crowns and molars. More complex machine learning models did not outperform less-complex ones.
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Affiliation(s)
| | | | | | | | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Aßmannshauser Street 4-6, 14197 Berlin, Germany; (S.R.H.); (V.P.); (J.K.); (A.K.)
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Mafla AC, De La Cruz-Rosero G, Vallejo-Rosero HW, Argoty-Rodríguez JA, Schwendicke F. Cariogenic diet consumption during lockdown. J Hum Nutr Diet 2023; 36:1539-1546. [PMID: 36628452 DOI: 10.1111/jhn.13138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 01/03/2023] [Indexed: 01/12/2023]
Abstract
BACKGROUND During stressful situations such as pandemic-associated lockdowns, individuals' diets may change towards (cariogenic) 'comfort food'. This study assessed the dietary patterns during the lockdown in the Colombian population. METHODS A cross-sectional online survey was designed. A convenience sample of 489 adults was drawn, with 50% of them being in COVID-19 lockdown and the other being not or only partially in lockdown. The questionnaire collected data about the type and frequency of food consumed, with a special focus on cariogenic (i.e., rich in free sugars and starches) food. Descriptive analyses were performed, and a generalised linear model was estimated to predict the frequency of cariogenic diet consumption in this period of time. RESULTS Sweet whole wheat bread (38.2%, p = 0.005), flavoured milk (26.4%, p = 0.002), sugar-sweetened bubble gums (39.8%, p = 0.001), toffees (35.4%, p = 0.004), soft candies (e.g., gums) (35.4%, p = 0.018), chocolates (55.3%, p = 0.017), filled doughnuts (28.5%, p = 0.013) or grapes (51.2%, p = 0.002) were significantly consumed more during the lockdown. Multivariable generalised linear modelling showed being single, having children and being in lockdown were significantly associated with higher frequency of cariogenic food consumption. CONCLUSIONS Lockdown was found to be associated with detrimentally altered food consumption patterns and, specifically, a more cariogenic diet. Healthcare professionals should consider this when reopening services, and political decision-makers may want to reflect on the unwarranted side effect of lockdown.
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Affiliation(s)
- Ana C Mafla
- School of Dentistry, Universidad Cooperativa de Colombia, Pasto, Colombia
- Escuela Internacional de Doctorado, Universidad Rey Juan Carlos, Madrid, España
| | | | | | | | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité Universitätsmedizin Berlin, Campus Charité Mitte, Berlin, Germany
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Pfänder L, Schneider L, Büttner M, Krois J, Meyer-Lueckel H, Schwendicke F. Multi-modal deep learning for automated assembly of periapical radiographs. J Dent 2023; 135:104588. [PMID: 37348642 DOI: 10.1016/j.jdent.2023.104588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 03/23/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
OBJECTIVES Periapical radiographs are oftentimes taken in series to display all teeth present in the oral cavity. Our aim was to automatically assemble such a series of periapical radiographs into an anatomically correct status using a multi-modal deep learning model. METHODS 4,707 periapical images from 387 patients (on average, 12 images per patient) were used. Radiographs were labeled according to their field of view and the dataset split into a training, validation, and test set, stratified by patient. In addition to the radiograph the timestamp of image generation was extracted and abstracted as follows: A matrix, containing the normalized timestamps of all images of a patient was constructed, representing the order in which images were taken, providing temporal context information to the deep learning model. Using the image data together with the time sequence data a multi-modal deep learning model consisting of two residual convolutional neural networks (ResNet-152 for image data, ResNet-50 for time data) was trained. Additionally, two uni-modal models were trained on image data and time data, respectively. A custom scoring technique was used to measure model performance. RESULTS Multi-modal deep learning outperformed both uni-modal image-based learning (p<0.001) and time-based learning (p<0.05). The multi-modal deep learning model predicted tooth labels with an F1-score, sensitivity and precision of 0.79, respectively, and an accuracy of 0.99. 37 out of 77 patient datasets were fully correctly assembled by multi-modal learning; in the remaining ones, usually only one image was incorrectly labeled. CONCLUSIONS Multi-modal modeling allowed automated assembly of periapical radiographs and outperformed both uni-modal models. Dental machine learning models can benefit from additional data modalities. CLINICAL SIGNIFICANCE Like humans, deep learning models may profit from multiple data sources for decision-making. We demonstrate how multi-modal learning can assist assembling periapical radiographs into an anatomically correct status. Multi-modal learning should be considered for more complex tasks, as clinically a wealth of data is usually available and could be leveraged.
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Affiliation(s)
- L Pfänder
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, 14197 Berlin, Germany
| | - L Schneider
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, 14197 Berlin, Germany; ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - M Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, 14197 Berlin, Germany; ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - J Krois
- ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - H Meyer-Lueckel
- Department of Restorative, Preventive and Pediatric Dentistry, zmk Bern, University of Bern, Switzerland
| | - F Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, 14197 Berlin, Germany; ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland.
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Arsiwala-Scheppach LT, Castner N, Rohrer C, Mertens S, Kasneci E, Cejudo Grano de Oro JE, Krois J, Schwendicke F. Gaze patterns of dentists while evaluating bitewing radiographs. J Dent 2023; 135:104585. [PMID: 37301462 DOI: 10.1016/j.jdent.2023.104585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/15/2023] [Accepted: 06/07/2023] [Indexed: 06/12/2023] Open
Abstract
OBJECTIVES Understanding dentists' gaze patterns on radiographs may allow to unravel sources of their limited accuracy and develop strategies to mitigate them. We conducted an eye tracking experiment to characterize dentists' scanpaths and thus their gaze patterns when assessing bitewing radiographs to detect primary proximal carious lesions. METHODS 22 dentists assessed a median of nine bitewing images each, resulting in 170 datasets after excluding data with poor quality of gaze recording. Fixation was defined as an area of attentional focus related to visual stimuli. We calculated time to first fixation, fixation count, average fixation duration, and fixation frequency. Analyses were performed for the entire image and stratified by (1) presence of carious lesions and/or restorations and (2) lesion depth (E1/2: outer/inner enamel; D1-3: outer-inner third of dentin). We also examined the transitional nature of the dentists' gaze. RESULTS Dentists had more fixations on teeth with lesions and/or restorations (median=138 [interquartile range=87, 204]) than teeth without them (32 [15, 66]), p<0.001. Notably, teeth with lesions had longer fixation durations (407 milliseconds [242, 591]) than those with restorations (289 milliseconds [216, 337]), p<0.001. Time to first fixation was longer for teeth with E1 lesions (17,128 milliseconds [8813, 21,540]) than lesions of other depths (p = 0.049). The highest number of fixations were on teeth with D2 lesions (43 [20, 51]) and lowest on teeth with E1 lesions (5 [1, 37]), p<0.001. Generally, a systematic tooth-by-tooth gaze pattern was observed. CONCLUSIONS As hypothesized, while visually inspecting bitewing radiographic images, dentists employed a heightened focus on certain image features/areas, relevant to the assigned task. Also, they generally examined the entire image in a systematic tooth-by-tooth pattern.
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Affiliation(s)
- Lubaina T Arsiwala-Scheppach
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Switzerland.
| | - Nora Castner
- Department of Computer Science, University of Tuebingen, Tuebingen, Germany
| | - Csaba Rohrer
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Sarah Mertens
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Enkelejda Kasneci
- Department of Computer Science, Technical University of Munich, Germany
| | - Jose Eduardo Cejudo Grano de Oro
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dental Diagnostics and Digital Dentistry, Switzerland
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Schneider L, Rischke R, Krois J, Krasowski A, Büttner M, Mohammad-Rahimi H, Chaurasia A, Pereira NS, Lee JH, Uribe SE, Shahab S, Koca-Ünsal RB, Ünsal G, Martinez-Beneyto Y, Brinz J, Tryfonos O, Schwendicke F. Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs. J Dent 2023; 135:104556. [PMID: 37209769 DOI: 10.1016/j.jdent.2023.104556] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023] Open
Abstract
OBJECTIVE Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs. METHODS We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n = 143 to n = 1881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers. RESULTS For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability. CONCLUSION If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high. CLINICAL SIGNIFICANCE This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.
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Affiliation(s)
- Lisa Schneider
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Roman Rischke
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Joachim Krois
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Aleksander Krasowski
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Martha Büttner
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Hossein Mohammad-Rahimi
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Shahid Beheshti University of Medical Sciences, Tehran, Iran Dental school, Iran
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George's Medical University, Lucknow, India
| | - Nielsen S Pereira
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Private Practice in Oral and Maxillofacial Radiology, Rio de Janeiro, Brazil
| | - Jae-Hong Lee
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea
| | - Sergio E Uribe
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry Oral Health, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia
| | - Shahriar Shahab
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahed University of Medical Sciences, Tehran, Iran
| | - Revan Birke Koca-Ünsal
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology, Faculty of Dentistry, University of Kyrenia, Kyrenia, Cyprus
| | - Gürkan Ünsal
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | | | - Janet Brinz
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany
| | - Olga Tryfonos
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam, Amsterdam, the Netherlands
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland.
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Rokhshad R, Ducret M, Chaurasia A, Karteva T, Radenkovic M, Roganovic J, Hamdan M, Mohammad-Rahimi H, Krois J, Lahoud P, Schwendicke F. Ethical considerations on artificial intelligence in dentistry: A framework and checklist. J Dent 2023; 135:104593. [PMID: 37355089 DOI: 10.1016/j.jdent.2023.104593] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/26/2023] Open
Abstract
OBJECTIVE Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
| | - Maxime Ducret
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Faculty of Odontology, University Claude Bernard Lyon Il, University of Lyon, Lyon, France
| | - Akhilanand Chaurasia
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India; Faculty of Dentistry, University of Puthisashtra, Combodia
| | - Teodora Karteva
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Operative Dentistry and Endodontics, Faculty of Dental Medicine, Medical University Plovdiv, Bulgaria
| | - Miroslav Radenkovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Serbia
| | - Jelena Roganovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology in Dentistry, School of Dental medicine, University of Belgrade, Serbia
| | - Manal Hamdan
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; General Dental Sciences Department, Marquette University School of Dentistry, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pierre Lahoud
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Oral and MaxilloFacial Surgery & Imaging and Pathology- OMFS-IMPATH Research Group, KU Leuven, Belgium; Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Belgium
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Moharrami M, Farmer J, Singhal S, Watson E, Glogauer M, Johnson AEW, Schwendicke F, Quinonez C. Detecting dental caries on oral photographs using artificial intelligence: A systematic review. Oral Dis 2023. [PMID: 37392423 DOI: 10.1111/odi.14659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 05/19/2023] [Accepted: 06/15/2023] [Indexed: 07/03/2023]
Abstract
OBJECTIVES This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. METHODS Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. RESULTS Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%-94.3% and 42.8%-95.4%, respectively. Irrespective of the task, F1-scores were 68.3%-95.4% for professional cameras, 78.8%-87.6%, for intraoral cameras, and 42.8%-80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. CONCLUSION Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions.
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Affiliation(s)
- Mohammad Moharrami
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
| | - Julie Farmer
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
| | - Sonica Singhal
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Health Promotion, Chronic Disease and Injury Prevention Department, Public Health Ontario, Toronto, Canada
| | - Erin Watson
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Michael Glogauer
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Department of Dental Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
- Department of Dentistry, Centre for Advanced Dental Research and Care, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Alistair E W Johnson
- Program in Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Geneva, Switzerland
- Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Carlos Quinonez
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
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Ayad N, Schwendicke F, Krois J, van den Bosch S, Bergé S, Bohner L, Hanisch M, Vinayahalingam S. Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey. Head Face Med 2023; 19:23. [PMID: 37349791 PMCID: PMC10288769 DOI: 10.1186/s13005-023-00368-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023] Open
Abstract
The use of artificial intelligence (AI) in dentistry is rapidly evolving and could play a major role in a variety of dental fields. This study assessed patients' perceptions and expectations regarding AI use in dentistry. An 18-item questionnaire survey focused on demographics, expectancy, accountability, trust, interaction, advantages and disadvantages was responded to by 330 patients; 265 completed questionnaires were included in this study. Frequencies and differences between age groups were analysed using a two-sided chi-squared or Fisher's exact tests with Monte Carlo approximation. Patients' perceived top three disadvantages of AI use in dentistry were (1) the impact on workforce needs (37.7%), (2) new challenges on doctor-patient relationships (36.2%) and (3) increased dental care costs (31.7%). Major expected advantages were improved diagnostic confidence (60.8%), time reduction (48.3%) and more personalised and evidencebased disease management (43.0%). Most patients expected AI to be part of the dental workflow in 1-5 (42.3%) or 5-10 (46.8%) years. Older patients (> 35 years) expected higher AI performance standards than younger patients (18-35 years) (p < 0.05). Overall, patients showed a positive attitude towards AI in dentistry. Understanding patients' perceptions may allow professionals to shape AI-driven dentistry in the future.
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Affiliation(s)
- Nasim Ayad
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics and Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Stefanie van den Bosch
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Stefaan Bergé
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
| | - Lauren Bohner
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Marcel Hanisch
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
| | - Shankeeth Vinayahalingam
- Department of Oral and Maxillofacial Surgery, Hospital University Münster, 48149 Münster, Germany
- Department of Oral and Maxillofacial Surgery, Radboud University Nijmegen Medical Centre, P.O. Box 9101, 6500 HB Nijmegen, the Netherlands
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Corrêa YM, Posser RU, Karam SA, Costa FDS, Schwendicke F, Demarco FF, Corrêa MB. Association among oral health and academic performance: a longitudinal study in a university in Southern Brazil. Braz Oral Res 2023; 37:e046. [PMID: 37255066 DOI: 10.1590/1807-3107bor-2023.vol37.0046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 12/06/2022] [Indexed: 06/01/2023] Open
Abstract
This longitudinal study aimed to investigate the association between self-perceived oral health, oral-health-related quality-of-life (OHRQoL), toothache, and university students' academic performance or dropout. A cohort of 2,089 students from 64 different courses at a public university in southern Brazil was interviewed in 2016 regarding their self-perceived oral health (Locker instrument; dichotomized into good/poor), OHRQoL (Oral Impacts on Daily Performances instrument, OIDP) and having had any toothache over the last 6 months (yes/no). After three years (2020), the academic records of 1,870 of these students were assessed, their average grade over all courses evaluated, and their dropout status was determined. Multivariable linear or logistic regression adjusting for gender, skin color, age, family income and maternal education was used to associate oral health variables (self-perceived oral health, OIDP, toothache) and academic performance or dropout. In 2016, 28.6% reported negative self-perceived oral health through the Locker instrument and 31.4% had toothache in the last 6 months. Over the next three years, 36.2% had dropped out. In multivariable regression, toothache in the last 6 months had a decrease of 0.32 (β -0.32, CI95% -0.59; -0.04) points in the final grade and were 35% (OR 1.35 CI95% 1.08; 1.69) more likely to dropout than students without toothache. In conclusion, this study showed that worse oral health conditions may be associated with worse academic performance or dropping out.
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Affiliation(s)
- Yorrana Martins Corrêa
- Universidade Federal de Pelotas - UFPel, Graduate Program in Dentistry, Pelotas, RS, Brazil
| | - Renata Uliana Posser
- Universidade Federal de Pelotas - UFPel, Graduate Program in Epidemiology, Pelotas, RS, Brazil
| | - Sarah Arangurem Karam
- Universidade Federal de Pelotas - UFPel, Graduate Program in Dentistry, Pelotas, RS, Brazil
| | - Francine Dos Santos Costa
- Charité - Universitätsmedizin Berlin, Department for Oral Diagnostics, Digital Health and Health Services Research, Berlin, Germany
| | - Falk Schwendicke
- Charité - Universitätsmedizin Berlin, Department for Oral Diagnostics, Digital Health and Health Services Research, Berlin, Germany
| | | | - Marcos Britto Corrêa
- Universidade Federal de Pelotas - UFPel, Graduate Program in Dentistry, Pelotas, RS, Brazil
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Kanzow P, Kanzow AF, Wiegand A, Schwendicke F. Implementation of repairs in dental practice: Randomized behavior simulation trial. J Am Dent Assoc 2023:S0002-8177(23)00210-6. [PMID: 37212760 DOI: 10.1016/j.adaj.2023.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/07/2023] [Accepted: 04/12/2023] [Indexed: 05/23/2023]
Abstract
BACKGROUND Despite increasing evidence, dentists have not widely adopted repairs. The authors aimed to develop and test potential interventions targeting dentists' behavior. METHODS Problem-centered interviews were performed. Emerging themes were linked to the Behavior Change Wheel to develop potential interventions. The efficacy of 2 interventions was then tested in a postally delivered behavioral change simulation trial among German dentists (n = 1,472 per intervention). Dentists' stated repair behavior regarding 2 case vignettes was assessed. Statistical analysis was performed using McNemar test, Fisher exact test, and a generalized estimating equation model (P < .05). RESULTS Two interventions (guideline, treatment fee item) were developed on the basis of identified barriers. A total of 504 dentists participated in the trial (17.1% response rate). Both interventions significantly changed dentists' behavior toward repairs of composite and amalgam restorations, respectively (guideline: difference [Δ] = +7.8% and Δ = +17.6%, treatment fee item: Δ = +6.4% and Δ = +31.5%; adjusted P < .001). Dentists were more likely to consider repairs if they already performed repairs frequently (odds ratio [OR], 1.23; 95% CI, 1.14 to 1.34) or sometimes (OR, 1.08; 95% CI, 1.01 to 1.16), if they regarded repairs as highly successful (OR, 1.24; 95% CI, 1.04 to 1.48), if their patients preferred repairs over total replacements (OR, 1.12; 95% CI, 1.03 to 1.23), for partially defective composite restorations (OR, 1.46; 95% CI, 1.39 to 1.53), and after receiving 1 of the 2 behavioral interventions (OR, 1.15; 95% CI, 1.13 to 1.19). CONCLUSIONS Systematically developed interventions targeting dentists' repair behaviors are likely efficacious to promote repairs. PRACTICAL IMPLICATIONS Most partially defective restorations are replaced completely. Effective implementation strategies are required to change dentists' behavior. This trial was registered at https://www. CLINICALTRIALS gov. The registration number is NCT03279874 for the qualitative phase and NCT05335616 for the quantitative phase.
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Schwendicke F, Büttner M. Artificial intelligence: advances and pitfalls. Br Dent J 2023; 234:749-750. [PMID: 37237204 DOI: 10.1038/s41415-023-5855-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 05/28/2023]
Affiliation(s)
- Falk Schwendicke
- Professor and Head of Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany.
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Büttner M, Schwendicke F. Natural language processing in dentistry. Br Dent J 2023; 234:753. [PMID: 37237206 DOI: 10.1038/s41415-023-5854-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 05/28/2023]
Affiliation(s)
| | - Falk Schwendicke
- Professor and Head of Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany.
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Büttner M, Schneider L, Krasowski A, Krois J, Feldberg B, Schwendicke F. Impact of Noisy Labels on Dental Deep Learning-Calculus Detection on Bitewing Radiographs. J Clin Med 2023; 12:3058. [PMID: 37176499 PMCID: PMC10179289 DOI: 10.3390/jcm12093058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 04/14/2023] [Accepted: 04/19/2023] [Indexed: 05/15/2023] Open
Abstract
Supervised deep learning requires labelled data. On medical images, data is often labelled inconsistently (e.g., too large) with varying accuracies. We aimed to assess the impact of such label noise on dental calculus detection on bitewing radiographs. On 2584 bitewings calculus was accurately labeled using bounding boxes (BBs) and artificially increased and decreased stepwise, resulting in 30 consistently and 9 inconsistently noisy datasets. An object detection network (YOLOv5) was trained on each dataset and evaluated on noisy and accurate test data. Training on accurately labeled data yielded an mAP50: 0.77 (SD: 0.01). When trained on consistently too small BBs model performance significantly decreased on accurate and noisy test data. Model performance trained on consistently too large BBs decreased immediately on accurate test data (e.g., 200% BBs: mAP50: 0.24; SD: 0.05; p < 0.05), but only after drastically increasing BBs on noisy test data (e.g., 70,000%: mAP50: 0.75; SD: 0.01; p < 0.05). Models trained on inconsistent BB sizes showed a significant decrease of performance when deviating 20% or more from the original when tested on noisy data (mAP50: 0.74; SD: 0.02; p < 0.05), or 30% or more when tested on accurate data (mAP50: 0.76; SD: 0.01; p < 0.05). In conclusion, accurate predictions need accurate labeled data in the training process. Testing on noisy data may disguise the effects of noisy training data. Researchers should be aware of the relevance of accurately annotated data, especially when testing model performances.
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Affiliation(s)
- Martha Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
| | - Lisa Schneider
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
| | - Aleksander Krasowski
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, 14197 Berlin, Germany
| | - Joachim Krois
- ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
| | - Ben Feldberg
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, 14197 Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, 14197 Berlin, Germany
- ITU/WHO Focus Group AI4Health, Topic Group Dental Diagnostics and Digital Dentistry, CH-1211 Geneva 20, Switzerland
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Park W, Schwendicke F, Krois J, Huh JK, Lee JH. Identification of Dental Implant Systems Using a Large-Scale Multicenter Data Set. J Dent Res 2023:220345231160750. [PMID: 37085970 DOI: 10.1177/00220345231160750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2023] Open
Abstract
This study aimed to evaluate the efficacy of deep learning (DL) for the identification and classification of various types of dental implant systems (DISs) using a large-scale multicenter data set. We also compared the classification accuracy of DL and dental professionals. The data set, which was collected from 5 college dental hospitals and 10 private dental clinics, contained 37,442 (24.8%) periapical and 113,291 (75.2%) panoramic radiographic images and consisted of a total of 10 manufacturers and 25 different types of DISs. The classification accuracy of DL was evaluated using a pretrained and modified ResNet-50 architecture, and comparison of accuracy performance and reading time between DL and dental professionals was conducted using a self-reported questionnaire. When comparing the accuracy performance for classification of DISs, DL (accuracy: 82.0%; 95% confidence interval [CI], 75.9%-87.0%) outperformed most of the participants (mean accuracy: 23.5% ± 18.5%; 95% CI, 18.5%-32.3%), including dentists specialized (mean accuracy: 43.3% ± 20.4%; 95% CI, 12.7%-56.2%) and not specialized (mean accuracy: 16.8% ± 9.0%; 95% CI, 12.8%-20.9%) in implantology. In addition, DL tends to require lesser reading and classification time (4.5 min) than dentists who specialized (75.6 ± 31.0 min; 95% CI, 13.1-78.4) and did not specialize (91.3 ± 38.3 min; 95% CI, 74.1-108.6) in implantology. DL achieved reliable outcomes in the identification and classification of various types of DISs, and the classification accuracy performance of DL was significantly superior to that of specialized or nonspecialized dental professionals. DL as a decision support aid can be successfully used for the identification and classification of DISs encountered in clinical practice.
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Affiliation(s)
- W Park
- Department of Advanced General Dentistry, Yonsei University College of Dentistry, Seoul, Korea
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea
| | - F Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - J Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Berlin, Germany
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - J-K Huh
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea
- Department of Oral and Maxillofacial Surgery, Gangnam Severance Hospital, Yonsei University College of Dentistry, Seoul, Korea
| | - J-H Lee
- Korean Academy of Oral and Maxillofacial Implantology (KAOMI) Implant Research Institute, Seoul, Korea
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
- Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea
- Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
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Uhlen-Strand MM, Hovden EAS, Schwendicke F, Ansteinsson VE, Mdala I, Skudutyte-Rysstad R. Dental care for older adults in home health care services - practices, perceived knowledge and challenges among Norwegian dentists and dental hygienists. BMC Oral Health 2023; 23:222. [PMID: 37069568 PMCID: PMC10111733 DOI: 10.1186/s12903-023-02951-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 04/06/2023] [Indexed: 04/19/2023] Open
Abstract
BACKGROUND Providing dental services to dependent older adults might be challenging because of physical and cognitive decline. The present study aimed to explore current practices, knowledge, and experienced challenges related to the treatment of older adults in home health care services (HHCS) among dentists and dental hygienists in Norway. METHODS An electronic questionnaire survey was distributed to Norwegian dentists and dental hygienists, inquiring about background characteristics, current practices, self-perceived knowledge, and challenges when providing oral health care for older HHCS patients. RESULTS Four hundred and sixty-six dentists and 244 dental hygienists treating older HHCS patients responded to the survey. The majority were female (n=620; 87.3%) and worked in the public dental service (PDS) (n=639; 90%). When older HHCS adults attended the dental practice, the treatments provided were most frequently aimed at relieving acute oral problems, although dental hygienists reported to focus on improving oral health more often than dentists. Dentists reported to have more self-perceived knowledge than dental hygienists regarding patients with complex treatment needs, cognitive or physical impairment. Exploratory Factor Analysis (EFA) was carried out on the 16 items related to challenges, three factors were extracted and Structural Equation Models (SEMs) were performed. Challenges related to dental care for older HHCS adults were related to time, practical organization and communication. Variation within these categories was associated with sex, graduation year and country, as well as time used per patient and work sector, but not with professional status. CONCLUSIONS The results indicate that dental care for older HHCS patients is time-demanding and more often aimed at relieving symptoms than improving oral health. A substantial proportion of dentists and dental hygienists in Norway lack confidence when providing dental care for frail elderly.
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Affiliation(s)
| | | | | | - Vibeke Elise Ansteinsson
- Oral Health Centre of Expertise in Eastern Norway (OHCE-E), Oslo, Norway
- Department of Public Health Science, Faculty of Medicine, Institute of Health and Society, University of Oslo, Oslo, Norway
| | - Ibrahimu Mdala
- Oral Health Centre of Expertise in Eastern Norway (OHCE-E), Oslo, Norway
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Naumann M, Scholz P, Krois J, Schwendicke F, Sterzenbach G, Happe A. Monolithic hybrid abutment crowns (screw-retained) versus monolithic hybrid abutments with adhesively cemented monolithic crowns. Clin Oral Implants Res 2023; 34:209-220. [PMID: 36692161 DOI: 10.1111/clr.14031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 12/30/2022] [Accepted: 01/02/2023] [Indexed: 01/25/2023]
Abstract
OBJECTIVES The objective of this study is to compare monolithic hybrid abutment crowns (screw-retained) versus monolithic hybrid abutments with adhesively cemented monolithic single-tooth crowns. MATERIALS AND METHODS Twenty subjects in need of an implant-borne restoration were randomly assigned to receive either a cement-retained (CRR) or a screw-retained (SRR) implant-supported monolithic lithium disilicate (LS2 ) reconstruction. Each patient received a titanium implant with in internal conic connection. After osseointegration and second-stage surgery, healing abutments were placed for about 10 days. The type of restoration (CRR vs. SRR) was randomly assigned, and the restorations were manufactured of monolithic LS2 . Both types of restorations, CRR and SRR, were based on a titanium component (Ti-base) that was bonded to the abutment (CRR) or the crown (SRR). The follow-up period for all restoration was 36 months. Clinical outcome was evaluated according to Functional Implant Prosthetic Score (FIPS). Quality of live (OHIP) and patient's satisfaction were assessed using patient-reported outcome measures (PROMs). Primary endpoint was loss of restoration for any reason. Kaplan-Meier curves were constructed and log-rank testing was performed (p < .05). RESULTS One restoration of group CRR failed after 6 months due to loss of adhesion between Ti-base and individual abutment. No further biological or technical failures occurred. Kaplan-Meier analysis showed no significant difference between both treatment options (p = .317). There was no statistically significant difference between both types of restoration, neither for FIPS, OHIP, treatment time nor patient satisfaction (p > .05). CONCLUSION Monolithic hybrid abutment crowns (screw-retained) and monolithic hybrid abutment with adhesively cemented monolithic crowns using lithium disilicate showed no statistically significant difference for implant-based reconstructions in this pilot RCT setting.
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Affiliation(s)
- Michael Naumann
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders-Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Patricia Scholz
- Clinic for Dental Prosthetics, Center for Dental, Oral and Maxillofacial Medicine, University Hospital Ulm, Ulm, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Guido Sterzenbach
- Department of Prosthodontics, Geriatric Dentistry and Craniomandibular Disorders-Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Arndt Happe
- Clinic for Dental Prosthetics, Center for Dental, Oral and Maxillofacial Medicine, University Hospital Ulm, Ulm, Germany
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Göstemeyer G, Preus M, Elhennawy K, Schwendicke F, Paris S, Askar H. Accuracy of different approaches for detecting proximal root caries lesions in vitro. Clin Oral Investig 2023; 27:1143-1151. [PMID: 36112228 PMCID: PMC9985551 DOI: 10.1007/s00784-022-04709-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Accepted: 09/05/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVES The objective was to evaluate the diagnostic accuracy of radiographic evaluation (XR), visual-tactile assessment (VT), laser-fluorescence (LF) (DIAGNOdent Pen/KaVo), and near-infrared-light transillumination (NILT) (DIAGNOcam/KaVo) on proximal root caries lesions in vitro. METHODS Two-hundred extracted permanent premolars and molars with and without proximal root caries lesions were allocated to 50 diagnostic models simulating the proximal contacts between teeth and mounted in a phantom dummy head. Two independent examiners used the diagnostic approaches to detect any or advanced root caries lesions, with histologic evaluation of the lesions serving as reference. Receiver operating characteristic (ROC) curves were employed, and sensitivity, specificity, and the area under the ROC curve (AUC) are calculated. Significant differences in mean AUCs between approaches were assumed if p < 0.05 (two-sample t-test). RESULTS NILT was not applicable for proximal root caries detection. The sensitivity/specificity to detect any lesions was 0.81/0.63 for XR, 0.76/0.88 for VT and 0.81/0.95 for LF, and the sensitivity/specificity to detect advanced lesions was 0.43/0.94 for XR, 0.66/0.99 for VT, and 0.83/0.78 for LF, respectively. For both, any and advanced root caries lesions, mean AUCs for LF and VT were significantly higher compared to XR (p < 0.05). For any root caries lesions, LF was significantly more accurate than VT (p = 0.01), but not for advanced root caries lesions (p = 0.59). CONCLUSIONS Under the in vitro conditions chosen, LF and VT were more accurate than XR to detect proximal root caries lesions, with LF being particularly useful for initial lesion stages. CLINICAL RELEVANCE LF might be a useful diagnostic aid for proximal root caries diagnosis. Clinical studies are necessary to corroborate the findings.
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Affiliation(s)
- Gerd Göstemeyer
- Department of Operative, Preventive and Pediatric Dentistry, Charité - Universitätsmedizin Berlin, Assmannshauser Straße 4-6, 14197, Berlin, Germany.
| | - Mareike Preus
- Department of Operative, Preventive and Pediatric Dentistry, Charité - Universitätsmedizin Berlin, Assmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Karim Elhennawy
- Department of Orthodontics, Dentofacial Orthopedics and Pedodontics, Charité - Universitätsmedizin Berlin, Assmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Assmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Sebastian Paris
- Department of Operative, Preventive and Pediatric Dentistry, Charité - Universitätsmedizin Berlin, Assmannshauser Straße 4-6, 14197, Berlin, Germany
| | - Haitham Askar
- Department of Operative, Preventive and Pediatric Dentistry, Charité - Universitätsmedizin Berlin, Assmannshauser Straße 4-6, 14197, Berlin, Germany
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Schwendicke F, Herbst SR. Health economic evaluation of endodontic therapies. Int Endod J 2023; 56 Suppl 2:207-218. [PMID: 35488881 DOI: 10.1111/iej.13757] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/24/2022] [Accepted: 04/25/2022] [Indexed: 11/29/2022]
Abstract
Healthcare is an expensive endeavour, and it is likely that costs for endodontic treatment will grow over the next decade. The assessment of costs and, in most cases, health outcomes, and the comparison of the cost-health ratio of interventions, is at the heart of health economics. The present review aims to introduce the main concepts of health economic analysis, to systematically review the existing economic endodontic literature, and to deduce further action for the community. Overall, the identified body of evidence on the health economics of endodontic therapies is heterogenous and has several limitations: Not all studies identified robust data to inform their analyses and many relied on a wide range of assumptions, which were only explored for their impact in a limited way. However, a number of themes were identified from the review: (1) Maintaining pulpal vitality is preferable over root canal treatment if possible when it comes to cost-effectiveness. (2) Retaining teeth is usually more cost-effective than removing and replacing them. (3) Endodontic retreatment may be clinically indicated, but not always cost-effective, and should hence be considered carefully. In conclusion, the general sparsity of economic analyses is a concern, as decision makers such as commissioners or those funding dental care increasingly rely on them. The endodontic community is called to action to improve the competency of both researchers to conduct such analyses and consider them when planning research, but also clinicians who should factor in health economics when assigning interventions. Health economics should become an accepted pillar of endodontic research.
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Affiliation(s)
- Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sascha Rudolf Herbst
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Mohammad-Rahimi H, Rokhshad R, Bencharit S, Krois J, Schwendicke F. Deep learning: A primer for dentists and dental researchers. J Dent 2023; 130:104430. [PMID: 36682721 DOI: 10.1016/j.jdent.2023.104430] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 01/04/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
OBJECTIVES Despite deep learning's wide adoption in dental artificial intelligence (AI) research, researchers from other dental fields and, more so, dental professionals may find it challenging to understand and interpret deep learning studies, their employed methods, and outcomes. The objective of this primer is to explain the basic concept of deep learning. It will lay out the commonly used terms, and describe different deep learning approaches, their methods, and outcomes. METHODS Our research is based on the latest review studies, medical primers, as well as the state-of-the-art research on AI and deep learning, which have been gathered in the current study. RESULTS In this study, a basic understanding of deep learning models and various approaches to deep learning is presented. An overview of data management strategies for deep learning projects is presented, including data collection, data curation, data annotation, and data preprocessing. Additionally, we provided a step-by-step guide for completing a real-world project. CONCLUSION Researchers and clinicians can benefit from this study by gaining insight into deep learning. It can be used to critically appraise existing work or plan new deep learning projects. CLINICAL SIGNIFICANCE This study may be useful to dental researchers and professionals who are assessing and appraising deep learning studies within the field of dentistry.
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Affiliation(s)
- Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | - Rata Rokhshad
- Department of Medicine, Section of Endocrinology, Nutrition, and Diabetes, Vitamin D, Boston University Medical Center, Boston, MA, USA
| | - Sompop Bencharit
- Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, School of Dentistry, and Department of Biomedical Engineering, College of Engineering, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Federal Republic of Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Federal Republic of Germany.
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Cebula M, Göstemeyer G, Krois J, Pitchika V, Paris S, Schwendicke F, Effenberger S. Resin Infiltration of Non-Cavitated Proximal Caries Lesions in Primary and Permanent Teeth: A Systematic Review and Scenario Analysis of Randomized Controlled Trials. J Clin Med 2023; 12:jcm12020727. [PMID: 36675656 PMCID: PMC9864315 DOI: 10.3390/jcm12020727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 01/18/2023] Open
Abstract
The present study aimed to meta-analyze and evaluate the certainty of evidence for resin infiltration of proximal carious lesions in primary and permanent teeth. While resin infiltration has been shown efficacious for caries management, the certainty of evidence remains unclear. The protocol was registered with PROSPERO (CRD42018080895), and PRISMA guidelines have been followed. The databases PubMed, Embase, and Cochrane CENTRAL were systematically screened, complemented by hand searches and cross-referencing. Eleven relevant articles were identified and included, i.e., randomized controlled trials (RCTs) comparing the progression of resin infiltrated proximal caries lesions (combined with non-invasive measures) in primary or permanent teeth with non-invasive measures. Random-effects meta-analyses and trial sequential analyses (TSA) were performed for per-protocol (PP), intention-to-treat (ITT), and best/worst case (BC/WC) scenarios. Six included trials assessed lesions in permanent teeth and five trails assessed lesions in primary teeth. The trials had a high or unclear risk of bias. Risk of caries progression was significantly reduced for infiltrated lesions in the PP, ITT, and BC scenarios in both permanent teeth and primary teeth, but not in the WC scenario. According to the TSA, firm evidence was reached for all of the scenarios except the WC. In conclusion, there is firm evidence for resin infiltration arresting proximal caries lesions in permanent and primary teeth.
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Affiliation(s)
- Marcus Cebula
- Clinical Research Department, DMG Dental Material Gesellschaft mbH, Elbgaustraße 248, 22547 Hamburg, Germany
| | - Gerd Göstemeyer
- Department of Restorative, Preventive and Pediatric Dentistry, Charité—Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Vinay Pitchika
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Sebastian Paris
- Department of Restorative, Preventive and Pediatric Dentistry, Charité—Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
| | - Susanne Effenberger
- Clinical Research Department, DMG Dental Material Gesellschaft mbH, Elbgaustraße 248, 22547 Hamburg, Germany
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité—Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, 14197 Berlin, Germany
- Correspondence:
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Danek S, Büttner M, Krois J, Schwendicke F. How Do Users Respond to Mass Vaccination Centers? A Cross-Sectional Study Using Natural Language Processing on Online Reviews to Explore User Experience and Satisfaction with COVID-19 Vaccination Centers. Vaccines (Basel) 2023; 11:vaccines11010144. [PMID: 36679989 PMCID: PMC9861127 DOI: 10.3390/vaccines11010144] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/21/2022] [Accepted: 12/23/2022] [Indexed: 01/11/2023] Open
Abstract
To reach large groups of vaccine recipients, several high-income countries introduced mass vaccination centers for COVID-19. Understanding user experiences of these novel structures can help optimize their design and increase patient satisfaction and vaccine uptake. This study drew on user online reviews of vaccination centers to assess user experience and identify its key determinants over time, by sentiment, and by interaction. Machine learning methods were used to analyze Google reviews of six COVID-19 mass vaccination centers in Berlin from December 2020 to December 2021. 3647 user online reviews were included in the analysis. Of these, 89% (3261/3647) were positive according to user rating (four to five of five stars). A total of 85% (2740/3647) of all reviews contained text. Topic modeling of the reviews containing text identified five optimally latent topics, and keyword extraction identified 47 salient keywords. The most important themes were organization, friendliness/responsiveness, and patient flow/wait time. Key interactions for users of vaccination centers included waiting, scheduling, transit, and the vaccination itself. Keywords connected to scheduling and efficiency, such as "appointment" and "wait", were most prominent in negative reviews. Over time, the average rating score decreased from 4.7 to 4.1, and waiting and duration became more salient keywords. Overall, mass vaccination centers appear to be positively perceived, yet users became more critical over the one-year period of the pandemic vaccination campaign observed. The study shows that online reviews can provide real-time insights into newly set-up infrastructures, and policymakers should consider their use to monitor the population's response over time.
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Herbst SR, Herbst CS, Schwendicke F. Preoperative risk assessment does not allow to predict root filling length using machine learning: A longitudinal study. J Dent 2023; 128:104378. [PMID: 36442583 DOI: 10.1016/j.jdent.2022.104378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVES First we aimed to identify significant associations between preoperative risk factors and achieving optimal root filling length (RFL) during orthograde root canal treatments (RCT) and second to predict successful RFL using machine learning. METHODS Teeth receiving RCT at one university clinic from 2016-2020 with complete documentation were included. Successful RFL was defined to be 0-2mm of the apex, suboptimal RFL >2mm or beyond the apex. Logistic regression (logR) was used for association analyses; logR and more advanced machine learning (random forest (RF), support vector machine (SVM), decision tree (DT), gradient boosting machine (GBM) and extreme gradient boosting (XGB)) were employed for predictive modeling. RESULTS 555 completed RCT (343 patients, female/male 32.1/67.9%) were included. In our association analysis (involving the full dataset), unsuccessful RFL was more likely in undergraduate students (US): OR 2.74, 95% CI [1.61, 4.75], p < 0.001), teeth with indistinct canal paths (OR 11.04, [2.87, 44.88], p < 0.001), root canals reduced in size (OR 2.56, [1.49, 4.46], p < 0.01), retreatments (OR 3.13, [1.6, 6.41], p < 0.001). Subgroup analyses revealed that dentists were more successful in mitigating risks than undergraduate students. Prediction of RFL on a separate testset was limitedly possible regardless of the machine learning approach. CONCLUSIONS Achieving RFL is depending on the operator and several risk factors. The predictive performance on the technical outcome of a root canal treatment utilizing ML algorithms was insufficient. CLINICAL SIGNIFICANCE Preoperative risk assessment is a relevant step in endodontic treatment planning. Single radiographic risk factors were significantly associated with achieving (or not achieving) optimal RFL and showed higher predictive value than a more complex risk assessment form.
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Affiliation(s)
- S R Herbst
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Germany.
| | - C S Herbst
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Germany
| | - F Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Aßmannshauser Str. 4-6, Berlin 14197, Germany
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Schwendicke F, Chaurasia A, Wiegand T, Uribe SE, Fontana M, Akota I, Tryfonos O, Krois J. Artificial intelligence for oral and dental healthcare: Core education curriculum. J Dent 2023; 128:104363. [PMID: 36410581 DOI: 10.1016/j.jdent.2022.104363] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/09/2022] [Accepted: 11/11/2022] [Indexed: 11/22/2022] Open
Abstract
OBJECTIVES Artificial intelligence (AI) is swiftly entering oral health services and dentistry, while most providers show limited knowledge and skills to appraise dental AI applications. We aimed to define a core curriculum for both undergraduate and postgraduate education, establishing a minimum set of outcomes learners should acquire when taught about oral and dental AI. METHODS Existing curricula and other documents focusing on literacy of medical professionals around AI were screened and relevant items extracted. Items were scoped and adapted using expert interviews with members of the IADR's e-oral health group, the ITU/WHO's Focus Group AI for Health and the Association for Dental Education in Europe. Learning outcome levels were defined and each item assigned to a level. Items were systematized into domains and a curricular structure defined. The resulting curriculum was consented using an online Delphi process. RESULTS Four domains of learning outcomes emerged, with most outcomes being on the "knowledge" level: (1) Basic definitions and terms, the reasoning behind AI and the principle of machine learning, the idea of training, validating and testing models, the definition of reference tests, the contrast between dynamic and static AI, and the problem of AI being a black box and requiring explainability should be known. (2) Use cases, the required types of AI to address them, and the typical setup of AI software for dental purposes should be taught. (3) Evaluation metrics, their interpretation, the relevant impact of AI on patient or societal health outcomes and associated examples should be considered. (4) Issues around generalizability and representativeness, explainability, autonomy and accountability and the need for governance should be highlighted. CONCLUSION Both educators and learners should consider this core curriculum during planning, conducting and evaluating oral and dental AI education. CLINICAL SIGNIFICANCE A core curriculum on oral and dental AI may help to increase oral and dental healthcare providers' literacy around AI, allowing them to critically appraise AI applications and to use them consciously and on an informed basis.
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Affiliation(s)
- Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland.
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India
| | - Thomas Wiegand
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Technical University Berlin, Berlin, Germany
| | - Sergio E Uribe
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Bioinformatics Lab & Dept of Conservative Dentistry and Oral Health, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile
| | - Margherita Fontana
- Cariology, Restorative Sciences & Endodontics, University of Michigan, Ann Arbor, United States
| | - Ilze Akota
- Department of Oral and Maxillofacial surgery, Riga Stradins University, Riga, Latvia
| | - Olga Tryfonos
- ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland; Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherland
| | - Joachim Krois
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; ITU/WHO Focus Group AI on Health, Topic Group Dentistry, Switzerland
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