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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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Büttner M, Leser U, Schneider L, Schwendicke F. Natural Language Processing: Chances and Challenges in Dentistry. J Dent 2024; 141:104796. [PMID: 38072335 DOI: 10.1016/j.jdent.2023.104796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/21/2023] Open
Abstract
INTRODUCTION Natural language processing (NLP) is an intersection between Computer Science and Linguistic which aims to enable machines to process and understand human language. We here summarized applications and limitations of NLP in dentistry. DATA AND SOURCES Narrative review. FINDINGS NLP has evolved increasingly fast. For the dental domain, relevant NLP applications are text classification (e.g., symptom classification) and natural language generation and understanding (e.g., clinical chatbots assisting professionals in office work and patient communication). Analyzing large quantities of text will allow understanding diseases and their trajectories and support a more precise and personalized care. Speech recognition systems may serve as virtual assistants and facilitate automated documentation. However, to date, NLP has rarely been applied in dentistry. Existing research focuses mainly on rule-based solutions for narrow tasks. Technologies such as Recurrent Neural Networks and Transformers have been shown to surpass the language processing capabilities of such rule-based solutions in many fields, but are data-hungry (i.e., rely on large amounts of training data), which limits their application in the dental domain at present. Technologies such as federated or transfer learning or data sharing concepts may allow to overcome this limitation, while challenges in terms of explainability, reproducibility, generalizability and evaluation of NLP in dentistry remain to be resolved for enabling approval of such technologies in medical devices and services. CONCLUSIONS NLP will become a cornerstone of a number of applications in dentistry. The community is called to action to improve the current limitations and foster reliable, high-quality dental NLP. CLINICAL SIGNIFICANCE NLP for text classification (e.g., dental symptom classification) and language generation and understanding (e.g., clinical chatbots, speech recognition) will support administrative tasks in dentistry, provide deeper insights for clinicians and support research and education.
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Affiliation(s)
- Martha Büttner
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany.
| | - Ulf Leser
- Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lisa Schneider
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Germany
| | - Falk Schwendicke
- Clinic for Operative, Preventive and Pediatric Dentistry and Periodontology, Ludwig-Maximilians-University, Munich, Germany
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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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Carvalho J, Lotz M, Rubi L, Unger S, Pfister T, Buhmann J, Stadlinger B. Preinterventional Third-Molar Assessment Using Robust Machine Learning. J Dent Res 2023; 102:1452-1459. [PMID: 37944556 PMCID: PMC10683342 DOI: 10.1177/00220345231200786] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023] Open
Abstract
Machine learning (ML) models, especially deep neural networks, are increasingly being used for the analysis of medical images and as a supporting tool for clinical decision-making. In this study, we propose an artificial intelligence system to facilitate dental decision-making for the removal of mandibular third molars (M3M) based on 2-dimensional orthopantograms and the risk assessment of such a procedure. A total of 4,516 panoramic radiographic images collected at the Center of Dental Medicine at the University of Zurich, Switzerland, were used for training the ML model. After image preparation and preprocessing, a spatially dependent U-Net was employed to detect and retrieve the region of the M3M and inferior alveolar nerve (IAN). Image patches identified to contain a M3M were automatically processed by a deep neural network for the classification of M3M superimposition over the IAN (task 1) and M3M root development (task 2). A control evaluation set of 120 images, collected from a different data source than the training data and labeled by 5 dental practitioners, was leveraged to reliably evaluate model performance. By 10-fold cross-validation, we achieved accuracy values of 0.94 and 0.93 for the M3M-IAN superimposition task and the M3M root development task, respectively, and accuracies of 0.9 and 0.87 when evaluated on the control data set, using a ResNet-101 trained in a semisupervised fashion. Matthew's correlation coefficient values of 0.82 and 0.75 for task 1 and task 2, evaluated on the control data set, indicate robust generalization of our model. Depending on the different label combinations of task 1 and task 2, we propose a diagnostic table that suggests whether additional imaging via 3-dimensional cone beam tomography is advisable. Ultimately, computer-aided decision-making tools benefit clinical practice by enabling efficient and risk-reduced decision-making and by supporting less experienced practitioners before the surgical removal of the M3M.
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Affiliation(s)
- J.S. Carvalho
- ETH Zurich, Department of Computer Science, Zurich, Switzerland
- ETH AI Center, Zurich, Switzerland
| | - M. Lotz
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
| | - L. Rubi
- ETH Zurich, Department of Computer Science, Zurich, Switzerland
| | - S. Unger
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
| | - T. Pfister
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
| | - J.M. Buhmann
- ETH Zurich, Department of Computer Science, Zurich, Switzerland
- ETH AI Center, Zurich, Switzerland
| | - B. Stadlinger
- University of Zurich, Center for Dental Medicine, Zurich, Switzerland
- ETH AI Center, Zurich, Switzerland
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Schneider L, Rischke R, Krois J, Krasowski A, Büttner M, Mohammad-Rahimi H, Chaurasia A, Pereira NS, Lee JH, Uribe SE, Shahab S, Koca-Ünsal RB, Ünsal G, Martinez-Beneyto Y, Brinz J, Tryfonos O, Schwendicke F. Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs. J Dent 2023; 135:104556. [PMID: 37209769 DOI: 10.1016/j.jdent.2023.104556] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023] Open
Abstract
OBJECTIVE Federated Learning (FL) enables collaborative training of artificial intelligence (AI) models from multiple data sources without directly sharing data. Due to the large amount of sensitive data in dentistry, FL may be particularly relevant for oral and dental research and applications. This study, for the first time, employed FL for a dental task, automated tooth segmentation on panoramic radiographs. METHODS We employed a dataset of 4,177 panoramic radiographs collected from nine different centers (n = 143 to n = 1881 per center) across the globe and used FL to train a machine learning model for tooth segmentation. FL performance was compared against Local Learning (LL), i.e., training models on isolated data from each center (assuming data sharing not to be an option). Further, the performance gap to Central Learning (CL), i.e., training on centrally pooled data (based on data sharing agreements) was quantified. Generalizability of models was evaluated on a pooled test dataset from all centers. RESULTS For 8 out of 9 centers, FL outperformed LL with statistical significance (p<0.05); only the center providing the largest amount of data FL did not have such an advantage. For generalizability, FL outperformed LL across all centers. CL surpassed both FL and LL for performance and generalizability. CONCLUSION If data pooling (for CL) is not feasible, FL is shown to be a useful alternative to train performant and, more importantly, generalizable deep learning models in dentistry, where data protection barriers are high. CLINICAL SIGNIFICANCE This study proves the validity and utility of FL in the field of dentistry, which encourages researchers to adopt this method to improve the generalizability of dental AI models and ease their transition to the clinical environment.
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Affiliation(s)
- Lisa Schneider
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Roman Rischke
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, Berlin, Germany
| | - Joachim Krois
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Aleksander Krasowski
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Martha Büttner
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland
| | - Hossein Mohammad-Rahimi
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Shahid Beheshti University of Medical Sciences, Tehran, Iran Dental school, Iran
| | - Akhilanand Chaurasia
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Oral Medicine and Radiology, Faculty of Dental Sciences, King George's Medical University, Lucknow, India
| | - Nielsen S Pereira
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Private Practice in Oral and Maxillofacial Radiology, Rio de Janeiro, Brazil
| | - Jae-Hong Lee
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology, College of Dentistry and Institute of Oral Bioscience, Jeonbuk National University, Jeonju, Korea
| | - Sergio E Uribe
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry Oral Health, Riga Stradins University, Riga, Latvia; School of Dentistry, Universidad Austral de Chile, Valdivia, Chile; Baltic Biomaterials Centre of Excellence, Headquarters at Riga Technical University, Riga, Latvia
| | - Shahriar Shahab
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahed University of Medical Sciences, Tehran, Iran
| | - Revan Birke Koca-Ünsal
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology, Faculty of Dentistry, University of Kyrenia, Kyrenia, Cyprus
| | - Gürkan Ünsal
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Near East University, Nicosia, Cyprus
| | | | - Janet Brinz
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Conservative Dentistry and Periodontology, University Hospital, LMU Munich, Munich, Germany
| | - Olga Tryfonos
- ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland; Department of Periodontology and Oral Biochemistry, Academic Centre for Dentistry Amsterdam, Amsterdam, the Netherlands
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health, and Health Services Research, Charité - University Medicine Berlin, Berlin, Germany; ITU/WHO Focus Group on AI for Health, Topic Group Dental Diagnostics and Digital Dentistry, Geneva, Switzerland.
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Ueda A, Tussie C, Kim S, Kuwajima Y, Matsumoto S, Kim G, Satoh K, Nagai S. Classification of Maxillofacial Morphology by Artificial Intelligence Using Cephalometric Analysis Measurements. Diagnostics (Basel) 2023; 13:2134. [PMID: 37443528 DOI: 10.3390/diagnostics13132134] [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: 04/04/2023] [Revised: 06/08/2023] [Accepted: 06/15/2023] [Indexed: 07/15/2023] Open
Abstract
The characteristics of maxillofacial morphology play a major role in orthodontic diagnosis and treatment planning. While Sassouni's classification scheme outlines different categories of maxillofacial morphology, there is no standardized approach to assigning these classifications to patients. This study aimed to create an artificial intelligence (AI) model that uses cephalometric analysis measurements to accurately classify maxillofacial morphology, allowing for the standardization of maxillofacial morphology classification. This study used the initial cephalograms of 220 patients aged 18 years or older. Three orthodontists classified the maxillofacial morphologies of 220 patients using eight measurements as the accurate classification. Using these eight cephalometric measurement points and the subject's gender as input features, a random forest classifier from the Python sci-kit learning package was trained and tested with a k-fold split of five to determine orthodontic classification; distinct models were created for horizontal-only, vertical-only, and combined maxillofacial morphology classification. The accuracy of the combined facial classification was 0.823 ± 0.060; for anteroposterior-only classification, the accuracy was 0.986 ± 0.011; and for the vertical-only classification, the accuracy was 0.850 ± 0.037. ANB angle had the greatest feature importance at 0.3519. The AI model created in this study accurately classified maxillofacial morphology, but it can be further improved with more learning data input.
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Affiliation(s)
- Akane Ueda
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
- Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
| | - Cami Tussie
- DMD Candidate Class of 2025, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
| | - Sophie Kim
- DMD Candidate Class of 2025, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
| | - Yukinori Kuwajima
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
| | - Shikino Matsumoto
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
| | - Grace Kim
- Department of Developmental Biology, Harvard School of Dental Medicine,188 Longwood Avenue, Boston, MA 02115, USA
| | - Kazuro Satoh
- Division of Orthodontics, Department of Developmental Oral Health Science, School of Dentistry, Iwate Medical University, 1-3-27 Chuo-dori, Morioka 020-8505, Iwate, Japan
| | - Shigemi Nagai
- Department of Restorative Dentistry and Biomaterial Sciences, Harvard School of Dental Medicine, 188 Longwood Avenue, Boston, MA 02115, USA
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Ducret M, Mörch CM, Karteva T, Fisher J, Schwendicke F. Artificial intelligence for sustainable oral healthcare. J Dent 2022; 127:104344. [PMID: 36273625 DOI: 10.1016/j.jdent.2022.104344] [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: 12/30/2021] [Revised: 08/24/2022] [Accepted: 10/19/2022] [Indexed: 11/06/2022] Open
Abstract
OBJECTIVES Oral health is grounded in the United National (UN) 2030 Agenda for Sustainable Developement and its 17 Goals (SDGs), in particular SDG 3 (Ensure healthy lives and promote well-being for all at all ages). The World Health Organization (WHO) Global Strategy on Oral Health calls for prioritizing environmentally sustainable and less invasive oral health care, and planetary health. Artificial Intelligence (AI) has the potential to power the next generation of oral health services and care, however its relationship with the broader UN and WHO concepts of sustainability remains poorly defined and articulated. We review the double-edged relationships between AI and oral health, to suggest actions that promote a sustainable deployment of AI for oral health. DATA Concepts regarding AI, sustainability and sustainable development were identified and defined. A review of several double-edged relationship between AI and SDGs were exposed for the field of Oral Health. SOURCES Medline and international declarations of the WHO, the UN and the World Dental Federation (FDI) were screened. STUDY SELECTION One the one hand, AI may reduce transportation, optimize care delivery (SDG 3 "Good Health and Well-Being", SDG 13 "Climate Action"), and increase accessibility of services and reduce inequality (SDG 10 "Reduced Inequalities", SDG 4 "Quality Education"). On the other hand, the deployment, implementation and maintenance of AI require significant resources (SDG 12 "Responsible Consumption and Production"), and costs for AI may aggravate inequalities. Also, AI may be biased, reinforcing inequalities (SDG 10) and discrimination (SDG 5), and may violate principles of security, privacy and confidentiality of personal information (SDG 16). CONCLUSIONS Systematic assessment of the positive impact and adverse effects of AI on sustainable oral health may help to foster the former and curb the latter based on evidence. CLINICAL SIGNIFICANCE If sustainability imperatives are actively taken into consideration, the community of oral health professionals should then employ AI for improving effectiveness, efficiency, and safety of oral healthcare; strengthen oral health surveillance; foster education and accessibility of care; ensure fairness, transparency and governance of AI for oral health; develop legislation and infrastructure to expand the use of digital health technologies including AI.
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Affiliation(s)
- Maxime Ducret
- Institut de Biologie et Chimie des Protéines, Laboratoire de Biologie Tissulaire et Ingénierie Thérapeutique, UMR 5305 CNRS, Université Lyon 1, Lyon, France; Faculté d'Odontologie, Université Lyon 1, Lyon, France; Hospices Civils de Lyon, Centre de soins Dentaires, Lyon, France.
| | - Carl-Maria Mörch
- FARI - AI for the Common Good Institute, Free University of Brussels, Brussels, Belgium
| | - Teodora Karteva
- Department of Operative Dentistry and Endodontics, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Julian Fisher
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin, Berlin, Germany
| | - Falk Schwendicke
- Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin, Berlin, Germany
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Machine Learning in Predicting Tooth Loss: A Systematic Review and Risk of Bias Assessment. J Pers Med 2022; 12:jpm12101682. [PMID: 36294820 PMCID: PMC9605501 DOI: 10.3390/jpm12101682] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 09/27/2022] [Accepted: 09/30/2022] [Indexed: 11/16/2022] Open
Abstract
Predicting tooth loss is a persistent clinical challenge in the 21st century. While an emerging field in dentistry, computational solutions that employ machine learning are promising for enhancing clinical outcomes, including the chairside prognostication of tooth loss. We aimed to evaluate the risk of bias in prognostic prediction models of tooth loss that use machine learning. To do this, literature was searched in two electronic databases (MEDLINE via PubMed; Google Scholar) for studies that reported the accuracy or area under the curve (AUC) of prediction models. AUC measures the entire two-dimensional area underneath the entire receiver operating characteristic (ROC) curves. AUC provides an aggregate measure of performance across all possible classification thresholds. Although both development and validation were included in this review, studies that did not assess the accuracy or validation of boosting models (AdaBoosting, Gradient-boosting decision tree, XGBoost, LightGBM, CatBoost) were excluded. Five studies met criteria for inclusion and revealed high accuracy; however, models displayed a high risk of bias. Importantly, patient-level assessments combined with socioeconomic predictors performed better than clinical predictors alone. While there are current limitations, machine-learning-assisted models for tooth loss may enhance prognostication accuracy in combination with clinical and patient metadata in the future.
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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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