Diagnostic accuracy and feasibility of artificial intelligence-driven smartphone imaging for dental caries detection: A systematic review
Abstract
Objectives This systematic review assesses the diagnostic accuracy, feasibility, and clinical performance of artificial intelligence (AI)-based smartphone imaging tools for detecting dental caries. Methods Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) guidelines, five databases: PubMed, Scopus, Web of Science, Embase, and Cochrane Library, were searched up to March 26, 2025. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD420251047689). Diagnostic accuracy and feasibility of AI-driven analysis of smartphone-based dental images for the detection of dental caries were assessed. Risk of bias and applicability were evaluated using QUADAS-2. Results Fourteen studies met the inclusion criteria. AI models, particularly YOLO variants, DenseNet201, and MobileNetV3, demonstrated high diagnostic accuracy, especially for cavitated lesions, with some outperforming junior dentists. Enhanced YOLO models achieved up to 85.5 % mean average precision. Tools were generally user-friendly and suitable for community or at-home screening. However, sensitivity for early or non-cavitated lesions varied. Conclusion AI-driven smartphone imaging shows promise as an accessible and reliable tool for caries detection, particularly in low-resource or remote settings. Further research is needed to improve early lesion detection, ensure clinical validation, and support…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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