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Tageldin MA, Yacout YM, Eid FY, Abdelhafiz SH. Accuracy of cephalometric landmark identification by artificial intelligence platform versus expert orthodontist in unilateral cleft palate patients: A retrospective study. Int Orthod 2025; 23:100990. [PMID: 39978248 DOI: 10.1016/j.ortho.2025.100990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2024] [Revised: 02/05/2025] [Accepted: 02/06/2025] [Indexed: 02/22/2025]
Abstract
OBJECTIVE The primary aim of the study was to evaluate the accuracy of automated artificial intelligence (AI) cephalometric landmark identification in cleft patients and compare it to landmarks identified by an expert orthodontist. The secondary objective was to compare cephalometric measurements obtained by both methods. MATERIAL AND METHODS A total of 112 pre-treatment lateral cephalometric radiographs of unilateral cleft palate patients were collected from the archives of the Department of Orthodontics, Faculty of Dentistry, Alexandria University following screening of all the records of patients treated in the period January 2019-December 2022 for eligibility. For each of the acquired radiographs, cephalometric tracing was performed by fully automated WebCeph™ landmark detection process and by manual identification of the landmarks by an expert orthodontist using OnyxCeph™ software. The traced radiographs were then imported into Photoshop software for evaluation of the (x,y) coordinates, in mm, for each of the identified landmarks (Primary outcome). Moreover, linear and angular measurements generated using WebCeph™ and OnyxCeph™ software were compared (secondary outcomes). RESULTS The coordinates of A-point, ANS, and Or showed statistically significant differences between both identification methods, with a mean difference between the two methods ranging between -0.86mm±2.15 and 3.15mm±6.07. None of the dental landmarks showed statistically significant differences between the two methods. None of the soft tissue landmarks showed statistically significant differences, except Ns y-coordinate. Several points showed clinically significant differences between both methods. The greatest mean differences in cephalometric measurements between the two methods were reported in nasolabial angle CotgSnLs (18.3±22.77̊) followed by Max1-NA (-8.86±17.46̊) and Max1-SN (-8.43±12.51̊). CONCLUSIONS The identification of cephalometric landmarks in cleft palate patients using the web-based AI platform is not as accurate as manual identification. Manual adjustment of landmarks following AI identification is advised.
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Affiliation(s)
- Mostafa A Tageldin
- Department of Orthodontics, Faculty of Dentistry, Alexandria University, Champollion St., Azarita, 21521 Alexandria, Egypt
| | - Yomna Mohamed Yacout
- Department of Orthodontics, Faculty of Dentistry, Alexandria University, Champollion St., Azarita, 21521 Alexandria, Egypt.
| | - Farah Y Eid
- Department of Orthodontics, Faculty of Dentistry, Alexandria University, Champollion St., Azarita, 21521 Alexandria, Egypt
| | - Sherief H Abdelhafiz
- Department of Orthodontics, Faculty of Dentistry, Alexandria University, Champollion St., Azarita, 21521 Alexandria, Egypt
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Kunz F, Stellzig-Eisenhauer A, Widmaier LM, Zeman F, Boldt J. Assessment of the quality of different commercial providers using artificial intelligence for automated cephalometric analysis compared to human orthodontic experts. J Orofac Orthop 2025; 86:145-160. [PMID: 37642657 PMCID: PMC12043786 DOI: 10.1007/s00056-023-00491-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 06/28/2023] [Indexed: 08/31/2023]
Abstract
PURPOSE The aim of this investigation was to evaluate the accuracy of various skeletal and dental cephalometric parameters as produced by different commercial providers that make use of artificial intelligence (AI)-assisted automated cephalometric analysis and to compare their quality to a gold standard established by orthodontic experts. METHODS Twelve experienced orthodontic examiners pinpointed 15 radiographic landmarks on a total of 50 cephalometric X‑rays. The landmarks were used to generate 9 parameters for orthodontic treatment planning. The "humans' gold standard" was defined by calculating the median value of all 12 human assessments for each parameter, which in turn served as reference values for comparisons with results given by four different commercial providers of automated cephalometric analyses (DentaliQ.ortho [CellmatiQ GmbH, Hamburg, Germany], WebCeph [AssembleCircle Corp, Seongnam-si, Korea], AudaxCeph [Audax d.o.o., Ljubljana, Slovenia], CephX [Orca Dental AI, Herzliya, Israel]). Repeated measures analysis of variances (ANOVAs) were calculated and Bland-Altman plots were generated for comparisons. RESULTS The results of the repeated measures ANOVAs indicated significant differences between the commercial providers' predictions and the humans' gold standard for all nine investigated parameters. However, the pairwise comparisons also demonstrate that there were major differences among the four commercial providers. While there were no significant mean differences between the values of DentaliQ.ortho and the humans' gold standard, the predictions of AudaxCeph showed significant deviations in seven out of nine parameters. Also, the Bland-Altman plots demonstrate that a reduced precision of AI predictions must be expected especially for values attributed to the inclination of the incisors. CONCLUSION Fully automated cephalometric analyses are promising in terms of timesaving and avoidance of individual human errors. At present, however, they should only be used under supervision of experienced clinicians.
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Affiliation(s)
- Felix Kunz
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany.
| | | | - Lisa Marie Widmaier
- Department of Orthodontics, University Hospital of Würzburg, Pleicherwall 2, 97070, Würzburg, Germany
| | - Florian Zeman
- Centre for Clinical Studies, University Hospital of Regensburg, Regensburg, Germany
| | - Julian Boldt
- Department of Prosthetic Dentistry, University Hospital of Würzburg, Würzburg, Germany
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Bagdy-Bálint R, Szabó G, Zováthi ÖH, Zováthi BH, Somorjai Á, Köpenczei C, Rózsa NK. Accuracy of automated analysis in cephalometry. J Dent Sci 2025; 20:830-843. [PMID: 40224041 PMCID: PMC11993017 DOI: 10.1016/j.jds.2024.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2024] [Revised: 09/19/2024] [Indexed: 04/15/2025] Open
Abstract
Background/purpose Artificial intelligence (AI) has been widely used in medicine, including orthodontics. The aim of this study was to investigate the training process of a cascaded Convolutional Neural Network (CNN), built for landmark detection on various qualities of lateral cephalograms and to determine the speed, reliability and clinical accuracy of an algorithm for orthodontic diagnosis. Materials and methods The CNN model was trained on a total of 1600 lateral cephalograms. After each training datasets (input of 400, 800, 1200, 1600 images) were added, the model was evaluated on a test set containing 78 images of varying quality. We measured the accuracy of AI-based landmark detection by statistical analysis of intra- and interexaminer distance errors, as well as examiner versus model predictions, furthermore by prognosis of consecutive diagnostic failures. Results There was a clear improvement in time efficiency (5.25 min), and substantial improvements were observed during the training process. In terms of accuracy, based on Euclidean distance error measurements, the best model provided more consistent dot tracing than two different examiners or the same examiner on two different occasions. Angular (0.05°-1.86°) and proportional (3.14%) errors, measured by the best model, were considered clinically acceptable. Conclusion The application of a proper AI-algorithm for orthodontic cephalometric analysis results in lower variability between models than the variability observed among experts. AI predictions supported the examiners in finding the correct location of the specific landmarks more accurately and in less time as the training of the automatic prediction model improved. Further research could investigate the therapeutic consequences.
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Affiliation(s)
- Réka Bagdy-Bálint
- Semmelweis University, Department of Paediatric Dentistry and Orthodontics, Budapest, Hungary
| | - Gergely Szabó
- Pázmány Péter Catholic University, Budapest, Hungary
| | - Örkény H. Zováthi
- Pázmány Péter Catholic University, Budapest, Hungary
- Ceph Assistant Ltd., Budapest, Hungary
| | | | - Ábris Somorjai
- Semmelweis University, Department of Paediatric Dentistry and Orthodontics, Budapest, Hungary
| | - Csenge Köpenczei
- Semmelweis University, Department of Paediatric Dentistry and Orthodontics, Budapest, Hungary
| | - Noémi Katinka Rózsa
- Semmelweis University, Department of Paediatric Dentistry and Orthodontics, Budapest, Hungary
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Koz S, Uslu-Akcam O. Artificial Intelligence-Supported and App-Aided Cephalometric Analysis: Which One Can We Trust? Diagnostics (Basel) 2025; 15:559. [PMID: 40075806 PMCID: PMC11899230 DOI: 10.3390/diagnostics15050559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2025] [Revised: 02/20/2025] [Accepted: 02/21/2025] [Indexed: 03/14/2025] Open
Abstract
Background: This study aimed to compare the reproducibility and reliability of the AI-supported WebCeph and app-aided OneCeph cephalometric analysis programs with a manual analysis method and to evaluate the analysis times. Methods: The study material consisted of pretreatment lateral cephalograms from 110 cases. Cephalometric analyses were performed manually, using the WebCeph program, and using the OneCeph application. A total of 11 skeletal, 6 dental, and 3 soft tissue parameters were measured. Cephalometric analyses of 30 randomly selected cases were performed again using three methods. The analysis times were recorded. Results: The WebCeph program and OneCeph application are highly compatible with the manual analysis method in terms of all parameters, except for SN measurement. It was found that the WebCeph program and the OneCeph application demonstrated moderate agreement in U1-NA distance measurement, while statistically high agreement was observed among all three methods for other dental parameters. It was determined that there was a moderate agreement among the methods in terms of nasolabial angle, whereas a statistically high level of agreement was found for the other soft tissue parameters. The analysis time was found to be the lowest in the WebCeph program and the highest in the manual analysis method. Conclusions: The WebCeph program and OneCeph application showed a high degree of compatibility with the manual analysis method, except for SN, SNA, Gonial angle, Articular angle, U1-NA distance and nasolabial angle measurements. Due to the higher correlation between OneCeph and the manual method, it can be concluded that the OneCeph application is the best alternative to the manual method.
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Affiliation(s)
- Senol Koz
- Private Practice, 34255 Istanbul, Turkey;
| | - Ozge Uslu-Akcam
- Department of Orthodontics, Faculty of Dentistry, Ankara Yıldırım Beyazıt University, 06220 Ankara, Turkey
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Surdu A, Budala DG, Luchian I, Foia LG, Botnariu GE, Scutariu MM. Using AI in Optimizing Oral and Dental Diagnoses-A Narrative Review. Diagnostics (Basel) 2024; 14:2804. [PMID: 39767164 PMCID: PMC11674583 DOI: 10.3390/diagnostics14242804] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Revised: 11/30/2024] [Accepted: 12/11/2024] [Indexed: 01/11/2025] Open
Abstract
Artificial intelligence (AI) is revolutionizing the field of oral and dental healthcare by offering innovative tools and techniques for optimizing diagnosis, treatment planning, and patient management. This narrative review explores the current applications of AI in dentistry, focusing on its role in enhancing diagnostic accuracy and efficiency. AI technologies, such as machine learning, deep learning, and computer vision, are increasingly being integrated into dental practice to analyze clinical images, identify pathological conditions, and predict disease progression. By utilizing AI algorithms, dental professionals can detect issues like caries, periodontal disease and oral cancer at an earlier stage, thus improving patient outcomes.
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Affiliation(s)
- Amelia Surdu
- Department of Oral Diagnosis, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Dana Gabriela Budala
- Department of Dentures, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Ionut Luchian
- Department of Periodontology, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
| | - Liliana Georgeta Foia
- Department of Biochemistry, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 Universitătii Street, 700115 Iasi, Romania
- St. Spiridon Emergency County Hospital, 700111 Iasi, Romania
| | - Gina Eosefina Botnariu
- Department of Internal Medicine II, Faculty of Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 16 Universitătii Street, 700115 Iasi, Romania
- Department of Diabetes, Nutrition and Metabolic Diseases, St. Spiridon Emergency County Hospital, 700111 Iasi, Romania
| | - Monica Mihaela Scutariu
- Department of Oral Diagnosis, Faculty of Dental Medicine, “Grigore T. Popa” University of Medicine and Pharmacy, 700115 Iasi, Romania
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O'Friel K, Chapple A, Ballard R, Armbruster P. Assessing AudaxCeph®'s cephalometric tracing technology versus a semi-automated approach for analyzing severe Class II and Class III skeletons. Int Orthod 2024; 22:100926. [PMID: 39378572 DOI: 10.1016/j.ortho.2024.100926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2024] [Revised: 09/18/2024] [Accepted: 09/19/2024] [Indexed: 10/10/2024]
Abstract
OBJECTIVE To evaluate the accuracy and precision of the AudaxCeph® fully automated software in identifying cephalometric landmarks on lateral cephalograms of Class II and Class III skeletal relationships, comparing its performance against experienced orthodontists using manual tracing within the same software environment. MATERIAL AND METHODS Sixty cephalograms depicting severe Class II or Class III skeletal discrepancies were assessed by two board-certified orthodontists and AudaxCeph®'s artificial intelligence automatic tracing software. Among these, 40 cases were classified as Class II and 20 as Class III. An X-Y axis was established at the bottom left corner of each cephalogram, and subsequent X and Y coordinates for the landmarks were exported to Excel. Thirteen cephalometric landmarks were identified and used for comparing manual and automatic tracing methods, with no alteration of landmark positions post-tracing. Measures of the X coordinate, Y coordinate, and radial distance for each landmark were compared using t-tests for equivalence with a 2mm margin, both against AudaxCeph®'s positions and intra-operator reliability. RESULTS Analysis revealed that while most operator measurements closely approximated AudaxCeph® values, discrepancies exceeding 2mm were notable at Gonion and Porion landmarks. Slight variability was noted in one instance during intra-examiner evaluation at the Gonion landmark. CONCLUSIONS This study concludes that AudaxCeph®'s artificial intelligence-driven automatic tracing of cephalograms offers a reliable and accurate method for orthodontic treatment planning across various skeletal types and severities. On average, it exhibits minimal discrepancies exceeding 2mm compared to manual operators, with notable variations observed primarily at the Gonion and Porion landmarks. While AudaxCeph® is an acceptable tool for cephalometric landmark location, it's accuracy still require the practitioner to verify some less reliable landmark locations.
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Affiliation(s)
- Katherine O'Friel
- Department of Orthodontics, School of Dentistry, LSU Health New Orleans, 1100 Florida Avenue, 70119 New Orleans, LA, USA
| | - Andrew Chapple
- Department of Interdisciplinary Oncology, School of Medicine, LSU Health, New Orleans, USA
| | - Richard Ballard
- Department of Orthodontics, School of Dentistry, LSU Health New Orleans, 1100 Florida Avenue, 70119 New Orleans, LA, USA.
| | - Paul Armbruster
- Department of Orthodontics, School of Dentistry, LSU Health New Orleans, 1100 Florida Avenue, 70119 New Orleans, LA, USA
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Chung EJ, Yang BE, Kang SH, Kim YH, Na JY, Park SY, On SW, Byun SH. Validation of 2D lateral cephalometric analysis using artificial intelligence-processed low-dose cone beam computed tomography. Heliyon 2024; 10:e39445. [PMID: 39583802 PMCID: PMC11584577 DOI: 10.1016/j.heliyon.2024.e39445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 07/19/2024] [Accepted: 10/15/2024] [Indexed: 11/26/2024] Open
Abstract
Objectives Traditional cephalometric radiographs depict a three-dimensional structure in a two-dimensional plane; therefore, errors may occur during a quantitative assessment. Cone beam computed tomography, on the other hand, minimizes image distortion, allowing essential areas to be observed without overlap. Artificial intelligence can be used to enhance low-dose cone beam computed tomography images. This study aimed to clinically validate the use of artificial intelligence-processed low-dose cone beam computed tomography for generating two-dimensional lateral cephalometric radiographs by comparing these artificial intelligence-enhanced radiographs with traditional two-dimensional lateral cephalograms and those derived from standard cone beam computed tomography. Methods Sixteen participants who had previously undergone both cone beam computed tomography and plain radiography were selected. Group I included standard lateral cephalometric radiographs. Group II included cone beam computed tomography-produced lateral cephalometric radiographs, and Group III included artificial intelligence-processed low-dose cone beam computed tomography-produced lateral cephalometric radiographs. Lateral cephalometric radiographs of the three groups were analyzed using an artificial intelligence-based cephalometric analysis platform. Results A total of six angles and five lengths were measured for dentofacial diagnosis. There were no significant differences in measurements except for nasion-menton among the three groups. Conclusions Low-dose cone beam computed tomography could be an efficient method for cephalometric analyses in dentofacial treatment. Artificial intelligence-processed low-dose cone beam computed tomography imaging procedures have the potential in a wide range of dental applications. Further research is required to develop artificial intelligence technologies capable of producing acceptable and effective outcomes in various clinical situations. Clinical significance Replacing standard cephalograms with cone beam computed tomography (CBCT) to evaluate the craniofacial relationship has the potential to significantly enhance the diagnosis and treatment of selected patients. The effectiveness of low-dose (LD)-CBCT was assessed in this study. The results indicated that lateral cephalograms reconstructed using LD-CBCT were comparable to standard lateral cephalograms.
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Affiliation(s)
- Eun-Ji Chung
- Department of Conservative Dentistry, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
| | - Byoung-Eun Yang
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
- Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Dental AI-Robotics Center, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
| | - Sam-Hee Kang
- Department of Conservative Dentistry, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
| | - Young-Hee Kim
- Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Department of Oral and Maxillofacial Radiology, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
| | - Ji-Yeon Na
- Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Dental AI-Robotics Center, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
- Department of Oral and Maxillofacial Radiology, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
| | - Sang-Yoon Park
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
- Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Dental AI-Robotics Center, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
| | - Sung-Woon On
- Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Department of Oral and Maxillofacial Surgery, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, 18450, Republic of Korea
| | - Soo-Hwan Byun
- Department of Oral and Maxillofacial Surgery, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
- Graduate School of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Institute of Clinical Dentistry, Hallym University, Chuncheon, 24252, Republic of Korea
- Dental AI-Robotics Center, Hallym University Sacred Heart Hospital, Anyang, 14066, Republic of Korea
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Sadek M, Alaskari O, Hamdan A. Accuracy of web-based automated versus digital manual cephalometric landmark identification. Clin Oral Investig 2024; 28:621. [PMID: 39482549 DOI: 10.1007/s00784-024-06021-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Accepted: 10/27/2024] [Indexed: 11/03/2024]
Abstract
AIM The purpose of this study was to assess the accuracy of two web-based automated cephalometric landmark identification and analysis programs. Manual landmark identification using Dolphin Imaging software was used as reference. MATERIALS AND METHODS 105 cephalograms were selected and divided into three groups of 35 subjects each, Class I, II and III. Radiographs were traced using Dolphin imaging software. WebCeph™ (South Korea) and Cephio™ (Poland) were used for the automated cephalometric analysis. Bland-Altman limits of agreement and the concordance correlation coefficient (CCC) were calculated. Kruskal Wallis test was used to compare the accuracy of WebCeph™ and Cephio™ measurements between the three groups. Mann-Whitney U test was used to compare the absolute difference between cephalometric measurements obtained using WebCeph™ and Cephio™. RESULTS The mean difference (MD) between AI and manually-derived measurements was less than 1 mm/degree and ranged from 0.01 to 0.8 except for upper lip protrusion (MD 1.35°), nasolabial angle (MD 5.01°), SN-GoGn (MD 1.41°), Ramus height (MD 1.46°), and IMPA (MD 1.94°). The mean CCC was 0.91 (range 0.60 to 0.96). No statistically significant differences were found between the three malocclusion groups for most of the measurements (P > 0.05). CONCLUSIONS For most of the measurements, automated cephalometric measurements were clinically acceptable. Few differences were found between Webceph™ and Cephio™ for most measurements. Measurements including SNA, SN-PP, IMPA as well as soft tissue measurements require extra consideration and manual adjustment of respective landmarks for higher precision and improved efficiency.
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Affiliation(s)
- Mais Sadek
- Program Director and Assistant Professor of Orthodontics, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates.
- Assistant Professor of Orthodontics, Faculty of Dentistry, Ain Shams University, Cairo, Egypt.
| | - Omar Alaskari
- Resident, College of Dental Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Ahmad Hamdan
- Dean and Professor of Orthodontics, College of Dental Medicine, University of Sharjah, United Arab Emirates, Sharjah, United Arab Emirates
- Professor of Orthodontics, University of Jordan, Amman, Jordan
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Mercier JP, Rossi C, Sanchez IN, Renovales ID, Sahagún PMP, Templier L. Reliability and accuracy of Artificial intelligence-based software for cephalometric diagnosis. A diagnostic study. BMC Oral Health 2024; 24:1309. [PMID: 39468520 PMCID: PMC11520516 DOI: 10.1186/s12903-024-05097-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 10/23/2024] [Indexed: 10/30/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) is revolutionizing cephalometric diagnosis in orthodontics, streamlining the patient assessments. This study aimed to assess the reliability, accuracy, and time consumption of artificial intelligence (AI)-based software compared to a conventional digital cephalometric analysis method on 2D lateral cephalogram. METHODS 408 lateral cephalometries were analysed using three methods: manual landmark localization, automatic localization, and semi-automatic localization with AI-based software. On each lateral cephalogram, 15 variables were selected, including skeletal, dental, and soft tissue measurements. The difference between the two AI-based software options (automatic and semi-automatic) was compared with the conventional digital technique. The time required to produce a complete cephalometric tracing was evaluated for each method using Student's t-test. RESULTS Statistically significant differences in the accuracy of landmark positioning were detected among the three different techniques (p < 0,01). However, it is noteworthy that almost all of these differences were not clinically significant. There was a small difference in accuracy between the semi-automatic AI-based option and conventional digital techniques. Regarding the time used for each technique, the automatic version was the fastest, followed by the semi-automatic option and the conventional digital technique. (p < 0,000). CONCLUSIONS The study showed a statistical difference in accuracy between the conventional digital technique and two AI-based software alternatives, but these differences were not clinically significant except for specific measurements. The semi-automatic option was more accurate than the automatic one and faster than conventional tracing. Further research is needed to confirm AI's accuracy in cephalometric tracing.
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Affiliation(s)
- Jean-Philippe Mercier
- Department of Orthodontics, University of Alfonso X el Sabio, Avenidad de la universidad,1, Villanueva de la Cañada, Madrid, 28691, Spain.
| | - Cecilia Rossi
- Clinica Odontoiatrica Lario, Via Strada Statale dei Giovi, 59, Grandate, Come, 22070, Italy
| | | | | | | | - Laura Templier
- Cabinet Templier, 167 rue Camille Desmoulins, Saint Quentin, 02100, France
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Bor S, Ciğerim SÇ, Kotan S. Comparison of AI-assisted cephalometric analysis and orthodontist-performed digital tracing analysis. Prog Orthod 2024; 25:41. [PMID: 39428414 PMCID: PMC11491421 DOI: 10.1186/s40510-024-00539-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 09/22/2024] [Indexed: 10/22/2024] Open
Abstract
BACKGROUND The aim of this study was to compare and evaluate three AI-assisted cephalometric analysis platforms-CephX, WeDoCeph, and WebCeph-with the traditional digital tracing method using NemoCeph software. MATERIAL AND METHOD A total of 1500 lateral cephalometric films that met the inclusion criteria were classified as Class I, Class II, and Class III. Subsequently, 40 patients were randomly selected from each class. These selected films were uploaded to 3 AI-assisted cephalometric analysis platforms and analyzed without any manual intervention. The same films were also analyzed by an orthodontist using the NemoCeph program. RESULTS The results revealed significant differences in key angular measurements (ANB, FMA, IMPA, and NLA) across Class I, II, and III patients when comparing the four cephalometric analysis methods (WebCeph, WeDoCeph, CephX, and NemoCeph). Notably, ANB (p < 0.05), FMA (p < 0.001), IMPA (p < 0.001), and NLA (p < 0.001) varied significantly. Linear measurements also differed, with significant differences in U1-NA (p = 0.002) and Co-A (p = 0.002) in certain classes. Repeated measurement analysis revealed variation in SNA (p = 0.011) and FMA (p = 0.030), particularly in the Class II NemoCeph group, suggesting method-dependent variability. CONCLUSION AI-assisted cephalometric analysis platforms such as WebCeph, WeDoCeph, and CephX give rise to notable variation in accuracy and reliability compared to traditional manual digital tracing, specifically in terms of angular and linear measurements. These results emphasize the importance of meticulous selection and assessment of analysis methods in orthodontic diagnostics and treatment planning.
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Affiliation(s)
- Sabahattin Bor
- Faculty of Dentistry, Department of Orthodontics, İnönü University, Malatya, Turkey.
| | | | - Seda Kotan
- Faculty of Dentistry, Department of Orthodontics, Van Yüzüncü Yıl University, Van, Turkey
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Baig N, Gyasudeen KS, Bhattacharjee T, Chaudhry J, Prasad S. Comparative evaluation of commercially available AI-based cephalometric tracing programs. BMC Oral Health 2024; 24:1241. [PMID: 39425100 PMCID: PMC11490107 DOI: 10.1186/s12903-024-05032-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2024] [Accepted: 10/08/2024] [Indexed: 10/21/2024] Open
Abstract
OBJECTIVES Compare the accuracy and diagnostic concordance of three commercially available AI-based lateral cephalometric tracing software. MATERIALS AND METHODS Sixty-three lateral cephalometric radiographs were analyzed using semi-automatic (Dolphin Imaging Systems LLC) and AI-based software programs (WebCeph™, Cephio, and Ceppro DDH Inc.). Intra- and inter-observer reliability were assessed for human expert measurements, and repeated-measures one-way ANOVA was used to compare the AI and human expert measurements. The diagnostic performance was evaluated using sensitivity and specificity tests. RESULTS Human expert reliability was excellent (ICC > 0.9) for most cephalometric parameters. Compared to human experts, significant differences were observed for all three AI-based cephalometric programs (WebCeph™ - 10 of 11, Cephio - 7 of 11, and Ceppro DDH Inc. - 7 of 11 cephalometric measurements). Variations exceeding two units were noted for most parameters, and differences in defining the sagittal and vertical skeletal patterns, dental, and soft tissue characteristics were observed. CONCLUSION All three AI-based tracing programs showed inaccuracies compared to human expert measurements and lacked reliability in measuring key cephalometric parameters. Clinicians should exercise caution when relying solely on AI-based analyses for orthodontic treatment planning and assessment.
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Affiliation(s)
- Nida Baig
- Department of Orthodontics, Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Kabir Syed Gyasudeen
- Department of Orthodontics, Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | | | - Jahanzeb Chaudhry
- Department of Oral Diagnostics and Surgical Sciences, Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Sabarinath Prasad
- Department of Orthodontics, Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.
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Lee Y, Pyeon JH, Han SH, Kim NJ, Park WJ, Park JB. A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis. APPLIED SCIENCES 2024; 14:7342. [DOI: 10.3390/app14167342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/10/2025]
Abstract
Background: Researchers have noted that the advent of artificial intelligence (AI) heralds a promising era, with potential to significantly enhance diagnostic and predictive abilities in clinical settings. The aim of this meta-analysis is to evaluate the discrepancies in identifying anatomical landmarks between AI and manual approaches. Methods: A comprehensive search strategy was employed, incorporating controlled vocabulary (MeSH) and free-text terms. This search was conducted by two reviewers to identify published systematic reviews. Three major electronic databases, namely, Medline via PubMed, the Cochrane database, and Embase, were searched up to May 2024. Results: Initially, 369 articles were identified. After conducting a comprehensive search and applying strict inclusion criteria, a total of ten studies were deemed eligible for inclusion in the meta-analysis. The results showed that the average difference in detecting anatomical landmarks between artificial intelligence and manual approaches was 0.35, with a 95% confidence interval (CI) ranging from −0.09 to 0.78. Additionally, the overall effect between the two groups was found to be insignificant. Upon further analysis of the subgroup of cephalometric radiographs, it was determined that there were no significant differences between the two groups in terms of detecting anatomical landmarks. Similarly, the subgroup of cone-beam computed tomography (CBCT) revealed no significant differences between the groups. Conclusions: In summary, the study concluded that the use of artificial intelligence is just as effective as the manual approach when it comes to detecting anatomical landmarks, both in general and in specific contexts such as cephalometric radiographs and CBCT evaluations.
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Affiliation(s)
- Yoonji Lee
- Orthodontics, Graduate School of Clinical Dental Science, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Jeong-Hye Pyeon
- Orthodontics, Graduate School of Clinical Dental Science, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Sung-Hoon Han
- Department of Orthodontics, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Na Jin Kim
- Medical Library, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Won-Jong Park
- Department of Oral and Maxillofacial Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Jun-Beom Park
- Department of Periodontics, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Dental Implantology, Graduate School of Clinical Dental Science, The Catholic University of Korea, Seoul 06591, Republic of Korea
- Department of Medicine, Graduate School, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Hendrickx J, Gracea RS, Vanheers M, Winderickx N, Preda F, Shujaat S, Jacobs R. Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis. Eur J Orthod 2024; 46:cjae029. [PMID: 38895901 PMCID: PMC11185929 DOI: 10.1093/ejo/cjae029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
OBJECTIVES This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. SEARCH METHODS An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. SELECTION CRITERIA Studies that employed AI for 2D or 3D cephalometric landmark detection were included. DATA COLLECTION AND ANALYSIS The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. RESULTS Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). CONCLUSION The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. REGISTRATION PROSPERO: CRD42022328800.
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Affiliation(s)
- Julie Hendrickx
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Rellyca Sola Gracea
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia
| | - Michiel Vanheers
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Nicolas Winderickx
- Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
| | - Flavia Preda
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Sohaib Shujaat
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- King Abdullah International Medical Research Center, Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh 14611, Kingdom of Saudi Arabia
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium
- Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, 3000 Leuven, Belgium
- Department of Dental Medicine, Karolinska Institutet, 141 04 Stockholm, Sweden
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La Rosa S, Quinzi V, Palazzo G, Ronsivalle V, Lo Giudice A. The Implications of Artificial Intelligence in Pedodontics: A Scoping Review of Evidence-Based Literature. Healthcare (Basel) 2024; 12:1311. [PMID: 38998846 PMCID: PMC11240988 DOI: 10.3390/healthcare12131311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/19/2024] [Accepted: 06/29/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has emerged as a revolutionary technology with several applications across different dental fields, including pedodontics. This systematic review has the objective to catalog and explore the various uses of artificial intelligence in pediatric dentistry. METHODS A thorough exploration of scientific databases was carried out to identify studies addressing the usage of AI in pediatric dentistry until December 2023 in the Embase, Scopus, PubMed, and Web of Science databases by two researchers, S.L.R. and A.L.G. RESULTS From a pool of 1301 articles, only 64 met the predefined criteria and were considered for inclusion in this review. From the data retrieved, it was possible to provide a narrative discussion of the potential implications of AI in the specialized area of pediatric dentistry. The use of AI algorithms and machine learning techniques has shown promising results in several applications of daily dental pediatric practice, including the following: (1) assisting the diagnostic and recognizing processes of early signs of dental pathologies, (2) enhancing orthodontic diagnosis by automating cephalometric tracing and estimating growth and development, (3) assisting and educating children to develop appropriate behavior for dental hygiene. CONCLUSION AI holds significant potential in transforming clinical practice, improving patient outcomes, and elevating the standards of care in pediatric patients. Future directions may involve developing cloud-based platforms for data integration and sharing, leveraging large datasets for improved predictive results, and expanding AI applications for the pediatric population.
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Affiliation(s)
- Salvatore La Rosa
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Quinzi
- Department of Life, Health & Environmental Sciences, Postgraduate School of Orthodontics, University of L’Aquila, 67100 L’Aquila, Italy
| | - Giuseppe Palazzo
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
| | - Vincenzo Ronsivalle
- Section of Oral Surgery, Department of General Surgery and Medical-Surgical Specialties, School of Dentistry, Policlinico Universitario “Gaspare Rodolico—San Marco”, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy;
| | - Antonino Lo Giudice
- Section of Orthodontics, Department of Medical-Surgical Specialties, School of Dentistry, University of Catania, Via Santa Sofia 78, 95123 Catania, Italy; (G.P.); (A.L.G.)
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Surendran A, Daigavane P, Shrivastav S, Kamble R, Sanchla AD, Bharti L, Shinde M. The Future of Orthodontics: Deep Learning Technologies. Cureus 2024; 16:e62045. [PMID: 38989357 PMCID: PMC11234326 DOI: 10.7759/cureus.62045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2024] [Accepted: 06/09/2024] [Indexed: 07/12/2024] Open
Abstract
Deep learning has emerged as a revolutionary technical advancement in modern orthodontics, offering novel methods for diagnosis, treatment planning, and outcome prediction. Over the past 25 years, the field of dentistry has widely adopted information technology (IT), resulting in several benefits, including decreased expenses, increased efficiency, decreased need for human expertise, and reduced errors. The transition from preset rules to learning from real-world examples, particularly machine learning (ML) and artificial intelligence (AI), has greatly benefited the organization, analysis, and storage of medical data. Deep learning, a type of AI, enables robots to mimic human neural networks, allowing them to learn and make decisions independently without the need for explicit programming. Its ability to automate cephalometric analysis and enhance diagnosis through 3D imaging has revolutionized orthodontic operations. Deep learning models have the potential to significantly improve treatment outcomes and reduce human errors by accurately identifying anatomical characteristics on radiographs, thereby expediting analytical processes. Additionally, the use of 3D imaging technologies such as cone-beam computed tomography (CBCT) can facilitate precise treatment planning, allowing for comprehensive examinations of craniofacial architecture, tooth movements, and airway dimensions. In today's era of personalized medicine, deep learning's ability to customize treatments for individual patients has propelled the field of orthodontics forward tremendously. However, it is essential to address issues related to data privacy, model interpretability, and ethical considerations before orthodontic practices can use deep learning in an ethical and responsible manner. Modern orthodontics is evolving, thanks to the ability of deep learning to deliver more accurate, effective, and personalized orthodontic treatments, improving patient care as technology develops.
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Affiliation(s)
- Aathira Surendran
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Pallavi Daigavane
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Sunita Shrivastav
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Ranjit Kamble
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Abhishek D Sanchla
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Lovely Bharti
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
| | - Mrudula Shinde
- Department of Orthodontics & Dentofacial Orthopaedics, Sharad Pawar Dental College & Hospital, Wardha, IND
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16
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Alhazmi N, Alsaeed S, Almutairi L, Almohammadi D. Accuracy of and dental students' preferences toward manual and digital cephalometric landmark identification: A randomized cross-over study. J Dent Educ 2024; 88:815-822. [PMID: 38343342 DOI: 10.1002/jdd.13471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/05/2024] [Accepted: 01/13/2024] [Indexed: 06/16/2024]
Abstract
OBJECTIVE To evaluate dental students' perceptions of manual and digital cephalometric landmark identification methods based on their preferences, difficulty level, and procedure time required to provide insights into the future of dental education, considering incorporating digital technology in dental schools. MATERIALS AND METHODS Fifty-five second-year dental students were randomly divided into two groups: (1) group A, students who performed manual landmark identification first, followed by digital method; and (2) group B, students who performed digital method first, followed by manual method. The duration of the procedure was recorded. Subsequently, all students completed a questionnaire regarding the difficulty they experienced using a visual analog scale and their preferences. Landmark identification accuracy was measured. RESULTS Digital landmark identification was preferred by 93% of students. The mean procedure time for digital method was significantly lower than that of manual method (13.00 ± 5.60 vs. 9.70 ± 4.60; p = 0.002). Group B completed manual and digital methods in a shorter time than group A. Group A experienced less difficulty with manual procedure than group B. However, statistically significant differences were not observed in the difficulty level of digital technique. A statistically significant difference in the mean accuracy was shown in favor of the manual method. However, this difference is clinically insignificant (p = 0.001). CONCLUSIONS Students considered digital method to be effective for learning and preferred it over manual method. Furthermore, digital landmark identification demonstrated better performance and was faster than manual method, suggesting that this must be incorporated in undergraduate dental education.
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Affiliation(s)
- Nora Alhazmi
- Department of Preventive Dental Science, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Ministry of the National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Suliman Alsaeed
- Department of Preventive Dental Science, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Ministry of the National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Lamia Almutairi
- Department of Preventive Dental Science, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Ministry of the National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Duaa Almohammadi
- Department of Preventive Dental Science, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- Ministry of the National Guard Health Affairs, Riyadh, Saudi Arabia
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17
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Tashkandi NE, Alnaqa NH, Al-Saif NM, Allam E. Accuracy of Gonial Angle Measurements Using Panoramic Imaging versus Lateral Cephalograms in Adults with Different Mandibular Divergence Patterns. J Multidiscip Healthc 2024; 17:1923-1929. [PMID: 38706500 PMCID: PMC11070157 DOI: 10.2147/jmdh.s463688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Accepted: 04/23/2024] [Indexed: 05/07/2024] Open
Abstract
Introduction Gonial angle is an important craniofacial parameter providing information about symmetry and vertical dimensions of the facial skeleton. It can be measured on panoramic radiographs and lateral cephalograms. Reliable assessment of the gonial angle is challenged by the superimpositions associated with lateral cephalograms. The aim of the current study was to assess the precision of panoramic imaging in measuring the gonial angles compared to lateral cephalograms in adult patients with different mandibular divergence patterns. Methods Panoramic radiographs and lateral cephalograms of 448 adults (18-30 years old) were utilized in the study. The gonial angle was determined on the lateral cephalograms using an online AI-driven assessment tool (WebCephTM) and compared to the panoramic measurements among the different gender, malocclusion, and mandibular divergence groups. Results Statistically significant differences were recorded between measurements taken on lateral cephalograms or panoramic radiographs (p=0.022). In addition, statistically significant differences were reported in gonial angle measurements on panoramic radiographs among the different mandibular divergence groups (p=0.004) for FMA (p=0.002) for Sn-GoMe. Conclusion While cephalometry is considered the gold standard tool for reliable gonial angle assessment, panoramic radiographs were more accurate in detecting the differences between the divergence groups in the current study.
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Affiliation(s)
- Nada E Tashkandi
- Preventative Department, Riyadh Elm University, Riyadh, Saudi Arabia
| | | | | | - Eman Allam
- Basic Dental Science Department, Oral and Dental Research Institute, National Research Centre, Cairo, Egypt
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Ajwa N, Binsaeed A, Aloud S, Alanazi R, Bin Mlafakh H, Alajmi D. A digitalized analysis of incisal changes among orthodontically treated patients: A retrospective comparative study. F1000Res 2024; 13:343. [PMID: 38988878 PMCID: PMC11234081 DOI: 10.12688/f1000research.145095.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/21/2024] [Indexed: 07/12/2024] Open
Abstract
Background To compare incisor angulation and/or position changes among orthodontically treated patients with metal brackets and clear aligners. Methods A total of sixty-two participants of both sexes, aged-16-40 years old, with CL I skeletal pattern and mild crowding following strict eligibility criteria were included. The patients were divided into two groups based on their treatment approach. Pre and post lateral cephalograms were collected from Riyadh Elm University (REU) and then digitally analyzed using WEBCEPH (Medical Image Analysis) software. Eight angular and two linear measurements were used for the assessment. Results The upper incisor angulation and position showed statistically significant differences when orthodontic clear aligners were used. In contrast, no significant difference was observed with the conventional orthodontic treatment. However, the upper incisal palatal root torque decreased after clear aligner therapy compared to conventional treatment. The inter-incisal angle demonstrated a significant increase with clear aligners compared to conventional treatment. Conclusions The current study revealed the importance of definitive guidelines upon and after treatment, in addition to determining incisor changes. Orthodontic clear aligners are distinct from conventional treatments in controlling the incisors' angulation and position. The expansion treatment modality precedes Interproximal reduction in increasing the arch perimeter.
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Affiliation(s)
- Nancy Ajwa
- Preventive Dentistry Department, Riyadh Elem University, Riyadh, Saudi Arabia
| | - Alhanouf Binsaeed
- Dental intern, College of Medicine and Dentistry, Riyadh Elm University, Ryadh, Saudi Arabia
| | - Shaikhah Aloud
- Dental intern, College of Medicine and Dentistry, Riyadh Elm University, Ryadh, Saudi Arabia
| | - Raneem Alanazi
- Dental intern, College of Medicine and Dentistry, Riyadh Elm University, Ryadh, Saudi Arabia
| | - Hind Bin Mlafakh
- Dental intern, College of Medicine and Dentistry, Riyadh Elm University, Ryadh, Saudi Arabia
| | - Dalal Alajmi
- Dental intern, College of Medicine and Dentistry, Riyadh Elm University, Ryadh, Saudi Arabia
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Jeong H, Han SS, Jung HI, Lee W, Jeon KJ. Perceptions and attitudes of dental students and dentists in South Korea toward artificial intelligence: a subgroup analysis based on professional seniority. BMC MEDICAL EDUCATION 2024; 24:430. [PMID: 38649951 PMCID: PMC11034023 DOI: 10.1186/s12909-024-05441-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 04/17/2024] [Indexed: 04/25/2024]
Abstract
BACKGROUND This study explored dental students' and dentists' perceptions and attitudes toward artificial intelligence (AI) and analyzed differences according to professional seniority. METHODS In September to November 2022, online surveys using Google Forms were conducted at 2 dental colleges and on 2 dental websites. The questionnaire consisted of general information (8 or 10 items) and participants' perceptions, confidence, predictions, and perceived future prospects regarding AI (17 items). A multivariate logistic regression analysis was performed on 4 questions representing perceptions and attitudes toward AI to identify highly influential factors according to position, age, sex, residence, and self-reported knowledge level about AI of respondents. Participants were reclassified into 2 subgroups based on students' years in school and 4 subgroups based on dentists' years of experience. The chi-square test or Fisher's exact test was used to determine differences between dental students and dentists and between subgroups for all 17 questions. RESULTS The study included 120 dental students and 96 dentists. Participants with high level of AI knowledge were more likely to be interested in AI compared to those with moderate or low level (adjusted OR 24.345, p < 0.001). Most dental students (60.8%) and dentists (67.7%) predicted that dental AI would complement human limitations. Dental students responded that they would actively use AI in almost all cases (40.8%), while dentists responded that they would use AI only when necessary (44.8%). Dentists with 11-20 years of experience were the most likely to disagree that AI could outperform skilled dentists (50.0%), and respondents with longer careers had higher response rates regarding the need for AI education in schools. CONCLUSIONS Knowledge level about AI emerged as the factor influencing perceptions and attitudes toward AI, with both dental students and dentists showing similar views on recognizing the potential of AI as an auxiliary tool. However, students' and dentists' willingness to use AI differed. Although dentists differed in their confidence in the abilities of AI, all dentists recognized the need for education on AI. AI adoption is becoming a reality in dentistry, which requires proper awareness, proper use, and comprehensive AI education.
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Affiliation(s)
- Hui Jeong
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
| | - Sang-Sun Han
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea
| | - Hoi-In Jung
- Department of Preventive Dentistry & Public Oral Health, Yonsei University College of Dentistry, Seoul, South Korea
| | - Wan Lee
- Department of Oral and Maxillofacial Radiology, Wonkwang University College of Dentistry, Iksan, South Korea
| | - Kug Jin Jeon
- Department of Oral and Maxillofacial Radiology, Yonsei University College of Dentistry, Seoul, South Korea.
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Guinot-Barona C, Alonso Pérez-Barquero J, Galán López L, Barmak AB, Att W, Kois JC, Revilla-León M. Cephalometric analysis performance discrepancy between orthodontists and an artificial intelligence model using lateral cephalometric radiographs. J ESTHET RESTOR DENT 2024; 36:555-565. [PMID: 37882509 DOI: 10.1111/jerd.13156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/10/2023] [Accepted: 10/11/2023] [Indexed: 10/27/2023]
Abstract
PURPOSE The purpose of the present clinical study was to compare the Ricketts and Steiner cephalometric analysis obtained by two experienced orthodontists and artificial intelligence (AI)-based software program and measure the orthodontist variability. MATERIALS AND METHODS A total of 50 lateral cephalometric radiographs from 50 patients were obtained. Two groups were created depending on the operator performing the cephalometric analysis: orthodontists (Orthod group) and an AI software program (AI group). In the Orthod group, two independent experienced orthodontists performed the measurements by performing a manual identification of the cephalometric landmarks and a software program (NemoCeph; Nemotec) to calculate the measurements. In the AI group, an AI software program (CephX; ORCA Dental AI) was selected for both the automatic landmark identification and cephalometric measurements. The Ricketts and Steiner cephalometric analyses were assessed in both groups including a total of 24 measurements. The Shapiro-Wilk test showed that the data was normally distributed. The t-test was used to analyze the data (α = 0.05). RESULTS The t-test analysis showed significant measurement discrepancies between the Orthod and AI group in seven of the 24 cephalometric parameters tested, namely the corpus length (p = 0.003), mandibular arc (p < 0.001), lower face height (p = 0.005), overjet (p = 0.019), and overbite (p = 0.022) in the Ricketts cephalometric analysis and occlusal to SN (p = 0.002) and GoGn-SN (p < 0.001) in the Steiner cephalometric analysis. The intraclass correlation coefficient (ICC) between both orthodontists of the Orthod group for each cephalometric measurement was calculated. CONCLUSIONS Significant discrepancies were found in seven of the 24 cephalometric measurements tested between the orthodontists and the AI-based program assessed. The intra-operator reliability analysis showed reproducible measurements between both orthodontists, except for the corpus length measurement. CLINICAL SIGNIFICANCE The artificial intelligence software program tested has the potential to automatically obtain cephalometric analysis using lateral cephalometric radiographs; however, additional studies are needed to further evaluate the accuracy of this AI-based system.
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Affiliation(s)
- Clara Guinot-Barona
- Department of Dental Orthodontics, Faculty of Medicine and Health Sciences, Catholic University of Valencia, Valencia, Spain
| | | | - Lidia Galán López
- Department of Dental Orthodontics, Faculty of Medicine and Health Sciences, Catholic University of Valencia, Valencia, Spain
| | - Abdul B Barmak
- Clinical Research and Biostatistics, Eastman Institute of Oral Health, University of Rochester Medical Center, Rochester, New York, USA
| | - Wael Att
- Department of Prosthodontics, University Hospital of Freiburg, Freiburg, Germany, USA
| | - John C Kois
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Private Practice, Seattle, Washington, USA
| | - Marta Revilla-León
- Kois Center, Seattle, Washington, USA
- Department of Restorative Dentistry, School of Dentistry, University of Washington, Seattle, Washington, USA
- Department of Prosthodontics, School of Dental Medicine, Tufts University, Boston, Massachusetts, USA
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Umer F, Adnan S, Lal A. Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review. BMC Oral Health 2024; 24:220. [PMID: 38347508 PMCID: PMC10860267 DOI: 10.1186/s12903-024-03970-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/02/2024] [Indexed: 02/15/2024] Open
Abstract
Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.
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Affiliation(s)
- Fahad Umer
- Department of Surgery, Section of Dentistry, The Aga Khan University, Karachi, Pakistan
| | - Samira Adnan
- Department of Operative Dentistry, Sindh Institute of Oral Health Sciences, Jinnah Sindh Medical University, Karachi, Pakistan
| | - Abhishek Lal
- Department of Medicine, Section of Gastroenterology, The Aga Khan University, Karachi, Pakistan.
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22
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Ferrillo M, Pandis N, Fleming PS. The effect of vertical skeletal proportions on overbite changes in untreated adolescents: a longitudinal evaluation. Angle Orthod 2024; 94:25-30. [PMID: 37655804 DOI: 10.2319/042823-310.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 07/01/2023] [Indexed: 09/02/2023] Open
Abstract
OBJECTIVES To evaluate the change in overbite within an untreated cohort from 9 to 18 years of age and to compare age-related changes in overbite depth based on vertical skeletal proportion. MATERIALS AND METHODS Lateral cephalograms were obtained from the American Association of Orthodontists Foundation (AAOF) Craniofacial Growth Legacy Collection Project. All cephalometric outcome measures were assessed at ages 9-11 (T1), 13-15 (T2), and 17-19 (T3) years. Generalized estimating equation (GEE) regression models were fit to examine the effect of MP-SN on overbite adjusted for age and gender. RESULTS A total of 130 subjects from the Denver, Bolton Brush, and Oregon Growth Studies were included. Overbite was relatively constant from T1 to T3 irrespective of facial type, with a minor decrease (0.15 mm) being observed overall. There was a transient increase between T1 and T2 (0.31 mm) that was canceled out by changes during later adolescence. Based on the GEE regression model adjusted for time and gender, a minor but statistically significantly greater reduction in overbite arose as MP-SN increased (coefficient = -0.080; 95% confidence interval -0.12, -0.04; P < .01). CONCLUSIONS In hyperdivergent subjects, a marginal decrease in overbite was observed from 9 to 18 years of age, with a transient increase from the period spanning 9-11 years to 13-15 years, which was negated in later adolescence. There are limited data to suggest that observation of vertical growth is required in most patients with marginally increased vertical facial proportions in the juvenile and pubertal phases.
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23
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Liu J, Zhang C, Shan Z. Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives. Healthcare (Basel) 2023; 11:2760. [PMID: 37893833 PMCID: PMC10606213 DOI: 10.3390/healthcare11202760] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023] Open
Abstract
In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.
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Affiliation(s)
- Junqi Liu
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Chengfei Zhang
- Division of Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
| | - Zhiyi Shan
- Division of Paediatric Dentistry and Orthodontics, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China;
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24
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Kiełczykowski M, Kamiński K, Perkowski K, Zadurska M, Czochrowska E. Application of Artificial Intelligence (AI) in a Cephalometric Analysis: A Narrative Review. Diagnostics (Basel) 2023; 13:2640. [PMID: 37627899 PMCID: PMC10453867 DOI: 10.3390/diagnostics13162640] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
In recent years, the application of artificial intelligence (AI) has become more and more widespread in medicine and dentistry. It may contribute to improved quality of health care as diagnostic methods are getting more accurate and diagnostic errors are rarer in daily medical practice. The aim of this paper was to present data from the literature on the effectiveness of AI in orthodontic diagnostics based on the analysis of lateral cephalometric radiographs. A review of the literature from 2009 to 2023 has been performed using PubMed, Medline, Scopus and Dentistry & Oral Sciences Source databases. The accuracy of determining cephalometric landmarks using widely available commercial AI-based software and advanced AI algorithms was presented and discussed. Most AI algorithms used for the automated positioning of landmarks on cephalometric radiographs had relatively high accuracy. At the same time, the effectiveness of using AI in cephalometry varies depending on the algorithm or the application type, which has to be accounted for during the interpretation of the results. In conclusion, artificial intelligence is a promising tool that facilitates the identification of cephalometric landmarks in everyday clinical practice, may support orthodontic treatment planning for less experienced clinicians and shorten radiological examination in orthodontics. In the future, AI algorithms used for the automated localisation of cephalometric landmarks may be more accurate than manual analysis.
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Affiliation(s)
| | | | | | | | - Ewa Czochrowska
- Department of Orthodontics, Medical University in Warsaw, 02-097 Warsaw, Poland; (M.K.); (K.K.); (K.P.); (M.Z.)
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25
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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: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [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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26
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Popova T, Stocker T, Khazaei Y, Malenova Y, Wichelhaus A, Sabbagh H. Influence of growth structures and fixed appliances on automated cephalometric landmark recognition with a customized convolutional neural network. BMC Oral Health 2023; 23:274. [PMID: 37165409 PMCID: PMC10173502 DOI: 10.1186/s12903-023-02984-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 04/20/2023] [Indexed: 05/12/2023] Open
Abstract
BACKGROUND One of the main uses of artificial intelligence in the field of orthodontics is automated cephalometric analysis. Aim of the present study was to evaluate whether developmental stages of a dentition, fixed orthodontic appliances or other dental appliances may affect detection of cephalometric landmarks. METHODS For the purposes of this study a Convolutional Neural Network (CNN) for automated detection of cephalometric landmarks was developed. The model was trained on 430 cephalometric radiographs and its performance was then tested on 460 new radiographs. The accuracy of landmark detection in patients with permanent dentition was compared with that in patients with mixed dentition. Furthermore, the influence of fixed orthodontic appliances and orthodontic brackets and/or bands was investigated only in patients with permanent dentition. A t-test was performed to evaluate the mean radial errors (MREs) against the corresponding SDs for each landmark in the two categories, of which the significance was set at p < 0.05. RESULTS The study showed significant differences in the recognition accuracy of the Ap-Inferior point and the Is-Superior point between patients with permanent dentition and mixed dentition, and no significant differences in the recognition process between patients without fixed orthodontic appliances and patients with orthodontic brackets and/or bands and other fixed orthodontic appliances. CONCLUSIONS The results indicated that growth structures and developmental stages of a dentition had an impact on the performance of the customized CNN model by dental cephalometric landmarks. Fixed orthodontic appliances such as brackets, bands, and other fixed orthodontic appliances, had no significant effect on the performance of the CNN model.
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Affiliation(s)
- Teodora Popova
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Thomas Stocker
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Yeganeh Khazaei
- Department of Statistics, Statistical Consultation Unit, StaBLab, LMU Munich, Akademiestr. 1, 80799, Munich, Germany
| | - Yoana Malenova
- Department of Oral and Maxillofacial Surgery, University Hospital, LMU Munich, Lindwurmstrasse 2a, 80337, Munich, Germany
| | - Andrea Wichelhaus
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany
| | - Hisham Sabbagh
- Department of Orthodontics and Dentofacial Orthopedics, University Hospital, LMU Munich, Goethestrasse 70, 80336, Munich, Germany.
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27
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Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 2023; 23:191. [PMID: 37005593 DOI: 10.1186/s12903-023-02881-8.pmid:37005593;pmcid:pmc10067288] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/14/2023] [Indexed: 05/26/2023] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. METHODS Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. RESULTS The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. CONCLUSION Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.
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Affiliation(s)
- Han Bao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Kejia Zhang
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Chenhao Yu
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Hu Li
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Dan Cao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China
- Centre de Recherche en Information Biomédicale Sino-Français, Rennes, 35000, France
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China
| | - Luwei Liu
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
| | - Bin Yan
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
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28
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Bao H, Zhang K, Yu C, Li H, Cao D, Shu H, Liu L, Yan B. Evaluating the accuracy of automated cephalometric analysis based on artificial intelligence. BMC Oral Health 2023; 23:191. [PMID: 37005593 PMCID: PMC10067288 DOI: 10.1186/s12903-023-02881-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 03/14/2023] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND The purpose of this study was to evaluate the accuracy of automatic cephalometric landmark localization and measurements using cephalometric analysis via artificial intelligence (AI) compared with computer-assisted manual analysis. METHODS Reconstructed lateral cephalograms (RLCs) from cone-beam computed tomography (CBCT) in 85 patients were selected. Computer-assisted manual analysis (Dolphin Imaging 11.9) and AI automatic analysis (Planmeca Romexis 6.2) were used to locate 19 landmarks and obtain 23 measurements. Mean radial error (MRE) and successful detection rate (SDR) values were calculated to assess the accuracy of automatic landmark digitization. Paired t tests and Bland‒Altman plots were used to compare the differences and consistencies in cephalometric measurements between manual and automatic analysis programs. RESULTS The MRE for 19 cephalometric landmarks was 2.07 ± 1.35 mm with the automatic program. The average SDR within 1 mm, 2 mm, 2.5 mm, 3 and 4 mm were 18.82%, 58.58%, 71.70%, 82.04% and 91.39%, respectively. Soft tissue landmarks (1.54 ± 0.85 mm) had the most consistency, while dental landmarks (2.37 ± 1.55 mm) had the most variation. In total, 15 out of 23 measurements were within the clinically acceptable level of accuracy, 2 mm or 2°. The rates of consistency within the 95% limits of agreement were all above 90% for all measurement parameters. CONCLUSION Automatic analysis software collects cephalometric measurements almost effectively enough to be acceptable in clinical work. Nevertheless, automatic cephalometry is not capable of completely replacing manual tracing. Additional manual supervision and adjustment for automatic programs can increase accuracy and efficiency.
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Affiliation(s)
- Han Bao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Kejia Zhang
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Chenhao Yu
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Hu Li
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Dan Cao
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China
| | - Huazhong Shu
- Laboratory of Image Science and Technology, Southeast University, Nanjing, 210096, China
- Centre de Recherche en Information Biomédicale Sino-Français, Rennes, 35000, France
- Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing, Southeast University, Nanjing, 210096, China
| | - Luwei Liu
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
| | - Bin Yan
- Department of Orthodontics, The Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Key Laboratory of Oral Diseases, Nanjing Medical University, Nanjing, 210029, China.
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Nanjing, 210029, China.
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29
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Almășan O, Leucuța DC, Hedeșiu M, Mureșanu S, Popa ȘL. Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis. J Clin Med 2023; 12:942. [PMID: 36769590 PMCID: PMC9918072 DOI: 10.3390/jcm12030942] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 01/27/2023] Open
Abstract
The aim was to systematically synthesize the current research and influence of artificial intelligence (AI) models on temporomandibular joint (TMJ) osteoarthritis (OA) diagnosis using cone-beam computed tomography (CBCT) or panoramic radiography. Seven databases (PubMed, Embase, Scopus, Web of Science, LILACS, ProQuest, and SpringerLink) were searched for TMJ OA and AI articles. We used QUADAS-2 to assess the risk of bias, while with MI-CLAIM we checked the minimum information about clinical artificial intelligence modeling. Two hundred and three records were identified, out of which seven were included, amounting to 10,077 TMJ images. Three studies focused on the diagnosis of TMJ OA using panoramic radiography with various transfer learning models (ResNet model) on which the meta-analysis was performed. The pooled sensitivity was 0.76 (95% CI 0.35-0.95) and the specificity was 0.79 (95% CI 0.75-0.83). The other studies investigated the 3D shape of the condyle and disease classification observed on CBCT images, as well as the numerous radiomics features that can be combined with clinical and proteomic data to investigate the most effective models and promising features for the diagnosis of TMJ OA. The accuracy of the methods was nearly equivalent; it was higher when the indeterminate diagnosis was excluded or when fine-tuning was used.
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Affiliation(s)
- Oana Almășan
- Department of Prosthetic Dentistry and Dental Materials, Iuliu Hațieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
| | - Daniel-Corneliu Leucuța
- Department of Medical Informatics and Biostatistics, Iuliu Hațieganu University of Medicine and Pharmacy, 400349 Cluj-Napoca, Romania
| | - Mihaela Hedeșiu
- Department of Oral and Maxillofacial Surgery and Implantology, Iuliu Hațieganu University of Medicine and Pharmacy, 400029 Cluj-Napoca, Romania
| | - Sorana Mureșanu
- Department of Oral and Maxillofacial Surgery and Implantology, Iuliu Hațieganu University of Medicine and Pharmacy, 400029 Cluj-Napoca, Romania
| | - Ștefan Lucian Popa
- 2nd Medical Department, Iuliu Hațieganu University of Medicine and Pharmacy, 400006 Cluj-Napoca, Romania
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30
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Hung KF, Yeung AWK, Bornstein MM, Schwendicke F. Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging. Dentomaxillofac Radiol 2023; 52:20220335. [PMID: 36472627 PMCID: PMC9793453 DOI: 10.1259/dmfr.20220335] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022] Open
Abstract
Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one's biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.
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Affiliation(s)
- Kuo Feng Hung
- Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Andy Wai Kan Yeung
- Division of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China
| | - Michael M. Bornstein
- Department of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin, Berlin, Germany
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