Diagnostic accuracy of deep learning for detecting dental caries on bitewing and periapical radiographs: a systematic review

Introduction: Deep learning models have been proposed to automate caries detection on intraoral radiographs, but reported accuracy varies widely and the evidence has not been synthesized specifically for bitewing and periapical projections. Methods: Following PRISMA 2020, PubMed, OpenAlex, and Crossref were searched for studies published from 2018 to 2025 that evaluated deep learning models for detecting, classifying, or segmenting dental caries on bitewing or periapical radiographs against a reference standard. Risk of bias was assessed with QUADAS-2. Results: Thirty-nine studies were included, most published in 2024 and 2025 and most using bitewing radiographs and YOLO architectures. Reported sensitivity ranged from 13.7% to 99.4%, specificity from 70.6% to 99.9%, and the area under the curve reached 0.954. The single histologically validated study reported sensitivity of 13.7% and specificity of 95.9%. Conclusions: Deep learning attains high but highly heterogeneous accuracy for caries detection on intraoral radiographs. Estimates often exceed the accuracy expected of expert readers but weaken when validated against histology, and prospective external validation is still lacking.