Detecting dental caries on oral photographs using artificial intelligence: A systematic review

Abstract

Objectives: This systematic review aimed at evaluating the performance of artificial intelligence (AI) models in detecting dental caries on oral photographs. Methods: Methodological characteristics and performance metrics of clinical studies reporting on deep learning and other machine learning algorithms were assessed. The risk of bias was evaluated using the quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool. A systematic search was conducted in EMBASE, Medline, and Scopus. Results: Out of 3410 identified records, 19 studies were included with six and seven studies having low risk of biases and applicability concerns for all the domains, respectively. Metrics varied widely and were assessed on multiple levels. F1-scores for classification and detection tasks were 68.3%–94.3% and 42.8%–95.4%, respectively. Irrespective of the task, F1-scores were 68.3%–95.4% for professional cameras, 78.8%–87.6%, for intraoral cameras, and 42.8%–80% for smartphone cameras. Limited studies allowed assessing AI performance for lesions of different severity. Conclusion: Automatic detection of dental caries using AI may provide objective verification of clinicians' diagnoses and facilitate patient-clinician communication and teledentistry. Future studies should consider more robust study designs, employ comparable and standardized metrics, and focus on the severity of caries lesions.