Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs

Szabó, Viktor; Szabó, Bence Tamás; Orhan, Kaan; Veres, Dániel Sándor; Manulis, David; Ezhov, Matvey; Sanders, Alex

doi:10.1016/j.jdent.2024.105105

Journal of Dentistry

2024

DOI: 10.1016/j.jdent.2024.105105

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Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs

Viktor Szabó,

Bence Tamás Szabó,

Kaan Orhan

et al.

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2024

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Cited by 3 publications

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Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis

Carvalho,

Nolden,

Wenning

et al. 2024

Journal of Dentistry

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Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis

Carvalho,

Nolden,

Wenning

et al. 2024

Journal of Dentistry

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Endodontic Treatment Outcomes in Cone Beam Computed Tomography Images—Assessment of the Diagnostic Accuracy of AI

Kazimierczak,

Issa

et al. 2024

JCM

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Background/Objectives: The aim of this study was to assess the diagnostic accuracy of the AI-driven platform Diagnocat for evaluating endodontic treatment outcomes using cone beam computed tomography (CBCT) images. Methods: A total of 55 consecutive patients (15 males and 40 females, aged 12–70 years) referred for CBCT imaging were included. CBCT images were analyzed using Diagnocat’s AI platform, which assessed parameters such as the probability of filling, adequate obturation, adequate density, overfilling, voids in filling, short filling, and root canal number. The images were also evaluated by two experienced human readers. Diagnostic accuracy metrics (accuracy, precision, recall, and F1 score) were assessed and compared to the readers’ consensus, which served as the reference standard. Results: The AI platform demonstrated high diagnostic accuracy for most parameters, with perfect scores for the probability of filling (accuracy, precision, recall, F1 = 100%). Adequate obturation showed moderate performance (accuracy = 84.1%, precision = 66.7%, recall = 92.3%, and F1 = 77.4%). Adequate density (accuracy = 95.5%, precision, recall, and F1 = 97.2%), overfilling (accuracy = 95.5%, precision = 86.7%, recall = 100%, and F1 = 92.9%), and short fillings (accuracy = 95.5%, precision = 100%, recall = 86.7%, and F1 = 92.9%) also exhibited strong performance. The performance of AI for voids in filling detection (accuracy = 88.6%, precision = 88.9%, recall = 66.7%, and F1 = 76.2%) highlighted areas for improvement. Conclusions: The AI platform Diagnocat showed high diagnostic accuracy in evaluating endodontic treatment outcomes using CBCT images, indicating its potential as a valuable tool in dental radiology.

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Developing an AI-Powered Algorithm for Automated Detection and Classification of Dental Caries from Intraoral Radiographs: A Machine Learning Approach

Haider

2024

Journal of Pharmacy and Bioallied Sciences

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This systematic review aims to assess the diagnostic accuracy of artificial intelligence (AI) models specifically developed for detecting and classifying dental caries. A comprehensive electronic literature search was performed on the PubMed, Web of Science, SCOPUS, and Embase databases. The search yielded a total of 397 results. We examined 10 articles that satisfied the selection criteria. In summary, AI-based models show high diagnostic accuracy in recognizing dental caries by analyzing dental radiography pictures.

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Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs

Cited by 3 publications

References 41 publications

Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis

Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis

Endodontic Treatment Outcomes in Cone Beam Computed Tomography Images—Assessment of the Diagnostic Accuracy of AI

Developing an AI-Powered Algorithm for Automated Detection and Classification of Dental Caries from Intraoral Radiographs: A Machine Learning Approach

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