AI-assisted bitewing radiographs have high accuracy for caries detection, reveals research

This systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs. This 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. Results: Of 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. AI 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.

Reference:

Bruna Katherine Guimarães Carvalho, Elias-Leon Nolden, Alexander Schulze Wenning, Szilvia Kiss-Dala, Gergely Agócs, Ivett Róth, Beáta Kerémi, Zoltán Géczi, Péter Hegyi, Márton Kivovics. Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis, Journal of Dentistry,

Volume 151,2024, 105388, ISSN 0300-5712,

https://doi.org/10.1016/j.jdent.2024.105388.

(https://www.sciencedirect.com/science/article/pii/S030057122400558X)

Keywords:

AI-assisted, bitewing, radiographs, high, accuracy, caries detection, reveals, research, Journal of Dentistry, Bruna Katherine Guimarães Carvalho, Elias-Leon Nolden, Alexander Schulze Wenning, Szilvia Kiss-Dala, Gergely Agócs, Ivett Róth, Beáta Kerémi, Zoltán Géczi, Péter Hegyi, Márton Kivovics