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Chen S, Yang Y, Wu W, Wei R, Wang Z, Tay FR, Hu J, Ma J. Classification of Caries Based on CBCT: A Deep Learning Network Interpretability Study. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01143-5. [PMID: 38806951 DOI: 10.1007/s10278-024-01143-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 04/16/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024]
Abstract
This study aimed to create a caries classification scheme based on cone-beam computed tomography (CBCT) and develop two deep learning models to improve caries classification accuracy. A total of 2713 axial slices were obtained from CBCT images of 204 carious teeth. Both classification models were trained and tested using the same pretrained classification networks on the dataset, including ResNet50_vd, MobileNetV3_large_ssld, and ResNet50_vd_ssld. The first model was used directly to classify the original images (direct classification model). The second model incorporated a presegmentation step for interpretation (interpretable classification model). Performance evaluation metrics including accuracy, precision, recall, and F1 score were calculated. The Local Interpretable Model-agnostic Explanations (LIME) method was employed to elucidate the decision-making process of the two models. In addition, a minimum distance between caries and pulp was introduced for determining the treatment strategies for type II carious teeth. The direct model that utilized the ResNet50_vd_ssld network achieved top accuracy, precision, recall, and F1 score of 0.700, 0.786, 0.606, and 0.616, respectively. Conversely, the interpretable model consistently yielded metrics surpassing 0.917, irrespective of the network employed. The LIME algorithm confirmed the interpretability of the classification models by identifying key image features for caries classification. Evaluation of treatment strategies for type II carious teeth revealed a significant negative correlation (p < 0.01) with the minimum distance. These results demonstrated that the CBCT-based caries classification scheme and the two classification models appeared to be acceptable tools for the diagnosis and categorization of dental caries.
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Affiliation(s)
- Surong Chen
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Yan Yang
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Weiwei Wu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Ruonan Wei
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China
| | - Zezhou Wang
- West China School of Stomatology, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Franklin R Tay
- Department of Endodontics, Dental College of Georgia, Augusta University, Augusta, GA, USA
| | - Jingyu Hu
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
| | - Jingzhi Ma
- Department of Stomatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
- School of Stomatology, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei Province, China.
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Samaranayake L. IDJ Pioneers Efforts to Reframe Dental Health Care Through Artificial Intelligence (AI). Int Dent J 2024; 74:177-178. [PMID: 38548452 PMCID: PMC10988283 DOI: 10.1016/j.identj.2024.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024] Open
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Dhanak N, Chougule VT, Nalluri K, Kakkad A, Dhimole A, Parihar AS. Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs. Bioinformation 2024; 20:243-247. [PMID: 38711998 PMCID: PMC11069605 DOI: 10.6026/973206300200243] [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: 03/01/2024] [Revised: 03/31/2024] [Accepted: 03/31/2024] [Indexed: 05/08/2024] Open
Abstract
Diagnosis of proximal caries is a difficult task. Artificial intelligence (AI) enabled diagnosis is gaining momentum. Therefore, it is of interest to evaluate the effectiveness of an artificial intelligence (AI) smart phone application for bitewing radiography towards real-time caries lesion detection. The Efficient Det-Lite1 artificial neural network was used after training 100 radiographic images obtained from the department of Oral Medicine. Trained model was then installed in a Google Pixel 6 (GP6) smartphone as artificial intelligence app. The back-facing mobile phone video camera of GP6 was utilised to detect caries lesions on 100 bitewing radiographs (BWR) with 80 carious lesion in real-time. Two different techniques such as scanning the static BWR on laptop with a moving mobile and scanning the moving radiograph on the laptop with stationery mobile were used. The average value of sensitivity/precision/F1 scores for both the techniques was 0.75/0.846 and 0.795 respectively. AI programme using the rear-facing mobile phone video camera was found to detect 75% of caries lesions in real time on 100 BWR with a precision of 84.6%. Thus, the use of AI with smart phone app is useful for caries diagnosis which is readily accessible, easy to use and fast.
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Affiliation(s)
- Nupur Dhanak
- Department of Conservative Dentistry and Endodontics, Government Dental College and Hospital, Ahmadabad, Gujarat, India
| | - Vaibhav T Chougule
- Department of Paediatric and Preventive Dentistry, Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Sangli, Maharashtra, India
| | | | - Ankur Kakkad
- Department of Oral Medicine and Radiology, Hitkarini Dental College and Hospital, Jabalpur, MP, India
| | - Ankit Dhimole
- Department of Oral Medicine and Radiology, Hitkarini Dental College and Hospital, Jabalpur, MP, India
| | - Anuj Singh Parihar
- Department of Periodontology, People's Dental Academy, Bhopal, Madhya Pradesh, India
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Kühnisch J, Aps JK, Splieth C, Lussi A, Jablonski-Momeni A, Mendes FM, Schmalz G, Fontana M, Banerjee A, Ricketts D, Schwendicke F, Douglas G, Campus G, van der Veen M, Opdam N, Doméjean S, Martignon S, Neuhaus KW, Horner K, Huysmans MCD. ORCA-EFCD consensus report on clinical recommendation for caries diagnosis. Paper I: caries lesion detection and depth assessment. Clin Oral Investig 2024; 28:227. [PMID: 38514502 PMCID: PMC10957694 DOI: 10.1007/s00784-024-05597-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/29/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVES The aim of the present consensus paper was to provide recommendations for clinical practice considering the use of visual examination, dental radiography and adjunct methods for primary caries detection. MATERIALS AND METHODS The executive councils of the European Organisation for Caries Research (ORCA) and the European Federation of Conservative Dentistry (EFCD) nominated ten experts each to join the expert panel. The steering committee formed three work groups that were asked to provide recommendations on (1) caries detection and diagnostic methods, (2) caries activity assessment and (3) forming individualised caries diagnoses. The experts responsible for "caries detection and diagnostic methods" searched and evaluated the relevant literature, drafted this manuscript and made provisional consensus recommendations. These recommendations were discussed and refined during the structured process in the whole work group. Finally, the agreement for each recommendation was determined using an anonymous Delphi survey. RESULTS Recommendations (N = 8) were approved and agreed upon by the whole expert panel: visual examination (N = 3), dental radiography (N = 3) and additional diagnostic methods (N = 2). While the quality of evidence was found to be heterogeneous, all recommendations were agreed upon by the expert panel. CONCLUSION Visual examination is recommended as the first-choice method for the detection and assessment of caries lesions on accessible surfaces. Intraoral radiography, preferably bitewing, is recommended as an additional method. Adjunct, non-ionising radiation methods might also be useful in certain clinical situations. CLINICAL RELEVANCE The expert panel merged evidence from the scientific literature with practical considerations and provided recommendations for their use in daily dental practice.
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Affiliation(s)
- Jan Kühnisch
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians Universität München, Poliklinik für Zahnerhaltung und Parodontologie, Goethestraße 70, 80336, München, Germany.
| | | | - Christian Splieth
- Preventive and Pediatric Dentistry, Center for Oral Health, Universitätsmedizin Greifswald, Greifswald, Germany
| | - Adrian Lussi
- University Hospital for Conservative Dentistry and Periodontology, Medical University of Innsbruck, Innsbruck, Austria
- Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland
| | | | - Fausto M Mendes
- Department of Pediatric Dentistry, School of Dentistry, University of São Paulo, São Paulo, Brazil
| | - Gottfried Schmalz
- Department of Conservative Dentistry and Periodontology, University Hospital Regensburg, Regensburg, Germany
- Department of Periodontology, University of Bern, Bern, Switzerland
| | - Margherita Fontana
- Department of Cariology, Restorative Sciences and Endodontics, University of Michigan School of Dentistry, Ann Arbor, USA
| | - Avijit Banerjee
- Conservative & MI Dentistry, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK
| | - David Ricketts
- Unit of Restorative Dentistry, University of Dundee, Dundee, UK
| | - Falk Schwendicke
- Department of Conservative Dentistry and Periodontology, University Hospital, Ludwig-Maximilians Universität München, Poliklinik für Zahnerhaltung und Parodontologie, Goethestraße 70, 80336, München, Germany
| | - Gail Douglas
- Department of Dental Public Health, University of Leeds Dental School, Leeds, UK
| | - Guglielmo Campus
- Department of Restorative, Preventive and Pediatric Dentistry, School of Dental Medicine, University of Bern, Bern, Switzerland
- Department of Surgery, Microsurgery and Medicine Sciences, School of Dentistry, University of Sassari, Sassari, Italy
| | - Monique van der Veen
- Departments of Preventive Dentistry and Paediatric Dentistry, Academic Centre for Dentistry Amsterdam, University of Amsterdam and VU University, Amsterdam, The Netherlands
- Oral Hygiene School, Inholland University of applied sciences, Amsterdam, The Netherlands
| | - Niek Opdam
- Department of Dentistry, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sophie Doméjean
- Centre de Recherche en Odontologie Clinique EA 4847, UFR d'Odontologie, Département d'Odontologie Conservatrice, Université Clermont Auvergne, Clermont-Ferrand, France
- Service d'Odontologie, CHU Estaing Clermont-Ferrand, Clermont-Ferrand, France
| | - Stefania Martignon
- UNICA - Caries Research Unit, Research Department, Universidad El Bosque, Bogotá, Colombia
| | - Klaus W Neuhaus
- Department of Pediatric Oral Health, University Center for Dental Medicine Basel (UZB), University of Basel, Basel, Switzerland
- Department of Dermatology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Keith Horner
- Division of Dentistry, School of Medical Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
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Dot G, Gajny L, Ducret M. [The challenges of artificial intelligence in odontology]. Med Sci (Paris) 2024; 40:79-84. [PMID: 38299907 DOI: 10.1051/medsci/2023199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024] Open
Abstract
Artificial intelligence has numerous potential applications in dentistry, as these algorithms aim to improve the efficiency and safety of several clinical situations. While the first commercial solutions are being proposed, most of these algorithms have not been sufficiently validated for clinical use. This article describes the challenges surrounding the development of these new tools, to help clinicians to keep a critical eye on this technology.
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Affiliation(s)
- Gauthier Dot
- UFR odontologie, université Paris Cité, Paris, France - AP-HP, hôpital Pitié-Salpêtrière, service de médecine bucco-dentaire, Paris, France - Institut de biomécanique humaine Georges Charpak, école nationale supérieure d'Arts et Métiers, Paris, France
| | - Laurent Gajny
- Institut de biomécanique humaine Georges Charpak, école nationale supérieure d'Arts et Métiers, Paris, France
| | - Maxime Ducret
- Faculté d'odontologie, université Claude Bernard Lyon 1, hospices civils de Lyon, Lyon, France
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Eggmann F, Weiger R, Zitzmann NU, Blatz MB. Implications of large language models such as ChatGPT for dental medicine. J ESTHET RESTOR DENT 2023; 35:1098-1102. [PMID: 37017291 DOI: 10.1111/jerd.13046] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 03/25/2023] [Accepted: 03/28/2023] [Indexed: 04/06/2023]
Abstract
OBJECTIVE This article provides an overview of the implications of ChatGPT and other large language models (LLMs) for dental medicine. OVERVIEW ChatGPT, a LLM trained on massive amounts of textual data, is adept at fulfilling various language-related tasks. Despite its impressive capabilities, ChatGPT has serious limitations, such as occasionally giving incorrect answers, producing nonsensical content, and presenting misinformation as fact. Dental practitioners, assistants, and hygienists are not likely to be significantly impacted by LLMs. However, LLMs could affect the work of administrative personnel and the provision of dental telemedicine. LLMs offer potential for clinical decision support, text summarization, efficient writing, and multilingual communication. As more people seek health information from LLMs, it is crucial to safeguard against inaccurate, outdated, and biased responses to health-related queries. LLMs pose challenges for patient data confidentiality and cybersecurity that must be tackled. In dental education, LLMs present fewer challenges than in other academic fields. LLMs can enhance academic writing fluency, but acceptable usage boundaries in science need to be established. CONCLUSIONS While LLMs such as ChatGPT may have various useful applications in dental medicine, they come with risks of malicious use and serious limitations, including the potential for misinformation. CLINICAL SIGNIFICANCE Along with the potential benefits of using LLMs as an additional tool in dental medicine, it is crucial to carefully consider the limitations and potential risks inherent in such artificial intelligence technologies.
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Affiliation(s)
- Florin Eggmann
- Department of Preventive and Restorative Sciences, Penn Dental Medicine, Robert Schattner Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- Department of Periodontology, Endodontology, and Cariology, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Roland Weiger
- Department of Periodontology, Endodontology, and Cariology, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Nicola U Zitzmann
- Department of Reconstructive Dentistry, University Center for Dental Medicine Basel UZB, University of Basel, Basel, Switzerland
| | - Markus B Blatz
- Department of Preventive and Restorative Sciences, Penn Dental Medicine, Robert Schattner Center, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Chan EK, Wah YY, Lam WYH, Chu CH, Yu OY. Use of Digital Diagnostic Aids for Initial Caries Detection: A Review. Dent J (Basel) 2023; 11:232. [PMID: 37886917 PMCID: PMC10605137 DOI: 10.3390/dj11100232] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 09/22/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023] Open
Abstract
The advance in digital diagnostic technologies has significantly facilitated the detection of dental caries. Despite the increase in clinically available digital diagnostic aids for dental caries, there is yet to be a comprehensive summary of all available technology. This review aims to provide an overview of digital diagnostic aids for the clinical detection of dental caries, particularly those at an initial stage. Currently available digital diagnostic aids for caries detection can be classified into four categories according to the initial source of energy, including radiation-based aids, light-based aids, ultrasound-based aids, and electric-based aids. Radiation-based aids use ionizing radiation, normally X-ray, to produce images of dental structures. Radiation-based aids encompass digital bitewing radiography and cone beam computed tomography. Light-based aids employ light or laser to induce signals for the detection of the changes in the carious dental hard tissue. Common light-based aids include digital transillumination and light/laser-induced fluorescence. Ultrasound-based aids detect the signal of ultrasound waves to assess the acoustic impedance of the carious teeth. The ultrasound caries detector is an available ultrasound-based aid. Electric-based aids assess the changes in the electric current conductance or impedance of the teeth with caries. Available electric-based aids include electrical conductance measurement and alternating current impedance spectroscopy. Except for these clinically available digital diagnostic aids, many digital diagnostic aids for caries detection are still under development with promising results in laboratory settings.
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Affiliation(s)
| | | | | | | | - Ollie Yiru Yu
- Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China (W.Y.-H.L.); (C.-H.C.)
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Rovira-Lastra B, Khoury-Ribas L, Flores-Orozco EI, Ayuso-Montero R, Chaurasia A, Martinez-Gomis J. Accuracy of digital and conventional systems in locating occlusal contacts: A clinical study. J Prosthet Dent 2023:S0022-3913(23)00481-X. [PMID: 37612195 DOI: 10.1016/j.prosdent.2023.06.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 06/20/2023] [Accepted: 06/21/2023] [Indexed: 08/25/2023]
Abstract
STATEMENT OF PROBLEM The accuracy of methods used for locating occlusal contacts throughout the entire clinical procedure has been poorly studied. PURPOSE The purpose of this clinical study was to determine the reproducibility and criterion validity for different methods of locating occlusal contacts. MATERIAL AND METHODS Thirty-two adults with natural dentitions participated in this cross-sectional test-retest study. In total, occlusal contacts at maximum intercuspation were recorded by using 15 methods: silicone transillumination with Occlufast Rock (40, 50, 100, and 200 µm) and Occlufast CAD (40 and 50 µm); virtual occlusion (100, 200, 300, and 400 µm); articulating film (12-, 40-, 100-, and 200-µm-thick); and T-Scan III. Images of the occlusal records were scaled and calibrated spatially, and the occlusal contacts of the right posterior mandibular teeth were delimited by using the FIJI software program. Reproducibility was expressed as 95% confidence intervals (95% CI) of the percentage of agreement in the location of the occlusal contacts between images from the test sessions against retest sessions using the same method. Criterion validity was expressed as 95% CI of the percentage of agreement in the location of the occlusal contacts between images from the test sessions against images from Occlufast Rock (criterion standard). RESULTS Occlufast Rock achieved 85% to 95% agreement in the location of the occlusal contacts between the 2 sessions, whereas Occlufast CAD, 200-µm articulating film, and T-Scan offered 79% to 86%, 68% to 75%, and 65% to 75% agreement, respectively. The most valid method was Occlufast CAD (74% to 80%) followed by the 200-µm articulating film (57% to 63%), 400-µm virtual occlusion (53% to 62%), 100-µm articulating film (52% to 60%), and T-Scan (48% to 56%). CONCLUSIONS Conventional methods, such as 100- and 200-µm articulating film and digital methods, including 400 µm virtual occlusion and T-Scan, offer sufficient accuracy in locating the occlusal contacts. However, strategies are needed to improve accuracy.
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Affiliation(s)
- Bernat Rovira-Lastra
- Assistant Professor, Department of Odontostomatology, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Catalonia, Spain
| | - Laura Khoury-Ribas
- Assistant Professor, Department of Odontostomatology, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Catalonia, Spain
| | - Elan-Ignacio Flores-Orozco
- Associate Professor, Department of Prosthodontics, Faculty of Dentistry, Autonomous University of Nayarit, Tepic, Mexico
| | - Raul Ayuso-Montero
- Associate Professor, Department of Odontostomatology, School of Dentistry, Faculty of Medicine and Health Sciences, University of Barcelona, Campus de Bellvitge 08907 L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain
| | - Akhilanand Chaurasia
- Associate Professor, Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India
| | - Jordi Martinez-Gomis
- Associate Professor, Serra Hunter Fellow, Department of Odontostomatology, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Catalonia, Spain; and Researcher, Oral Health and Masticatory System Group (Bellvitge Biomedical Research Institute) IDIBELL, L'Hospitalet de Llobregat, Barcelona, Catalonia, Spain.
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Cholan P, Ramachandran L, Umesh SG, P S, Tadepalli A. The Impetus of Artificial Intelligence on Periodontal Diagnosis: A Brief Synopsis. Cureus 2023; 15:e43583. [PMID: 37719493 PMCID: PMC10503663 DOI: 10.7759/cureus.43583] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2023] [Indexed: 09/19/2023] Open
Abstract
The current advances in digitized data additions, machine learning and computing framework, lead to the swiftly emerging concept of "Artificial Intelligence" (AI), that are developing into areas that were formerly contemplated for human expertise. AI is a relatively rapid paced mechanics wherein the computer technology is tuned to perform human tasks. An auxiliary domain of AI is machine learning (ML), and Deep learning, a subclass of ML technique comprehends multi-layer mathematical operations. AI-based applications have tremendous potential to improve and systematize patient care thereby alleviating dentists from laborious regular tasks, and facilitate personalized, predictive and preventive dentistry. In the dental clinic, AI can execute a variety of easy tasks with greater accuracy, minimal manpower, and with fewer mistakes over human equivalents. These tasks range from appointment scheduling and coordination to helping with clinical evaluation and therapy. Besides, this could assist in the early diagnosis of dental and maxillofacial abnormalities like periodontal ailments, root caries, bony lesions, and facial malformations in addition to automatically identifying and classifying dental restorations on digital radiographs. This brusque narrative review describes the AI-based systems, their respective applications in periodontal diagnosis, the multifarious studies, possible limitations and the predictable future of AI-based dental diagnostics and treatment planning.
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Affiliation(s)
- Priyanka Cholan
- Periodontics, Sri Ramaswamy Memorial (SRM) Dental College & Hospital, Chennai, IND
| | - Lakshmi Ramachandran
- Periodontics & Oral Implantology, Sri Ramaswamy Memorial (SRM) Dental College & Hospital, Chennai, IND
| | - Santo G Umesh
- Periodontics, Sri Ramaswamy Memorial (SRM) Dental College, Chennai, IND
| | - Sucharitha P
- Periodontics, Sri Ramaswamy Memorial (SRM) Dental College, Chennai, IND
| | - Anupama Tadepalli
- Periodontics & Oral Implantology, Sri Ramaswamy Memorial (SRM) Dental College & Hospital, Chennai, IND
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Turkkahraman H. Embracing the Unprecedented Pace of Change: Artificial Intelligence's Impact on Dentistry and Beyond. Eur J Dent 2023; 17:567-568. [PMID: 37473781 PMCID: PMC10569829 DOI: 10.1055/s-0043-1770913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023] Open
Affiliation(s)
- Hakan Turkkahraman
- Department of Orthodontics and Oral Facial Genetics, School of Dentistry, Indiana University, Indianapolis, Indiana, United States
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Affiliation(s)
- F Schwendicke
- Department of Oral Diagnostics, Digital Health, Health Services Research, Charité - Universitätsmedizin, Berlin, Germany
| | - M L Marazita
- Center for Craniofacial and Dental Genetics, Department of Oral and Craniofacial Sciences, School of Dental Medicine, and Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
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