ArticleFrontiers in dental medicine2025
Performance evaluation of AI-based caries detection technology and its educational training module: a dual-phase investigation.
Article in Frontiers in dental medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07639749 (Diagnostic Accuracy of Artificial Intelligence Analysis Using Intraoral Photographs Versus Clinical Examination in White Spot Lesions Detection, Identification, And Scoring.), which is not on this map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Diagnostic Accuracy of Artificial Intelligence Analysis Using Intraoral Photographs Versus Clinical Examination in White Spot Lesions Detection, Identification, And Scoring.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.Evidence-based dentistry · 2026Pooled it
- Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection Across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis.Diagnostics (Basel, Switzerland) · 2026Review
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Purpose: This objective of the study was to assess the accuracy of an AI-based caries detection system, Overjet Caries Assist, or OCA, (Overjet Inc. Claymont, DE, USA) and to analyze the efficacy of the AI-based caries detection training module in teaching dental students radiographic diagnosis of dental caries. Methods: Two independent calibrated observers evaluated 1604 proximal surfaces of teeth on intraoral bitewing radiographs and compared the findings to the OCA caries detection module. The sensitivity, specificity, positive and negative predictive values and diagnostic accuracy of the AI system were calculated. For the second part of the study, 82 first- and third-year dental students interpreted 10 intraoral bitewings for caries diagnosis before and after undergoing training with the OCA AI-caries detection training module. Non-parametric Wilcoxon signed-rank test was used to assess the difference between the students' learning before and after the Overjet module training. Results: The average sensitivity for enamel lesions was found to be 0.69, while the average sensitivity for dentinal lesions was found to be 0.91. The average specificity for enamel lesions was found to be 0.99, while the average specificity for dentinal lesions was found to be 0.98. There was a 43%-46% increase in students' ability to detect radiographic caries following the completion of the Overjet training module. Conclusion: The OCA module can be used as an effective tool for assisting diagnosis of caries among dental students. The Overjet training module is effective in training dental students' radiographic diagnosis of caries.
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