Abstract
ObjectivesThis systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs. MethodsThis study included randomized controlled trials (RCTs) and non-randomized controlled trials (non-RCTs) reporting on the diagnostic accuracy of AI for approximal carious lesions on bitewing radiographs. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A systematic search was conducted on November 4, 2023, in PubMed, Cochrane, and Embase databases and an updated search was performed on August 28, 2024. The primary outcomes assessed were sensitivity, specificity, and overall accuracy. Sensitivity and specificity were pooled using a bivariate model. ResultsOf the 2,442 studies identified, 21 met the inclusion criteria. The pooled sensitivity and specificity of AI were 0.94 (confidence interval (CI): ± 0.78–0.99) and 0.91 (CI: ± 0.84–0.95), respectively. The positive predictive value (PPV) ranged from 0.15 to 0.87, indicating a moderate capacity for identifying true positives among decayed teeth. The negative predictive value (NPV) ranged from 0.79 to 1.00, demonstrating a high ability to exclude healthy teeth. The diagnostic odds ratio was high, indicating strong overall diagnostic performance. ConclusionsAI models demonstrate clinically acceptable diagnostic accuracy for approximal caries on bitewing radiographs. Although AI can be valuable for preliminary screening, positive findings should be verified by dental experts to prevent unnecessary treatments and ensure timely diagnosis. AI models are highly reliable in excluding healthy approximal surfaces. Clinical significanceAI can assist dentists in detecting approximal caries on bitewing radiographs. However, expert supervision is required to prevent iatrogenic damage and ensure timely diagnosis.
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