Examining the diagnostic accuracy of artificial intelligence for detecting dental caries across a range of imaging modalities: An umbrella review with meta-analysis

Sarah Arzani, Ali Karimi, Pedram Iranmanesh, Maryam Yazdi, Mohammad A Sabeti, Mohammad Hossein Nekoofar, Jafar Kolahi, Heejung Bang and Paul MH Dummer

PLOS ONE, 2025, vol. 20, issue 8, 1-24

Abstract: The objective of this systematic review was to systematically collect and analyze multiple published systematic reviews to address the following research question “Are artificial intelligence (AI) algorithms effective for the detection of dental caries?”. A systematic search of five electronic databases, including the Cochrane Library, Embase, PubMed, Scopus, and Web of Science, was conducted until October 15, 2024, with a language restriction to English. All fourteen systematic reviews which assessed the performance of AI algorithms for the detection of dental caries were included. From 137 primary original research studies within the systematic reviews, only 20 reported the data necessary for inclusion in the meta-analysis. Pooled sensitivity was 0.85 (95% Confidence Interval (CI): 0.83 to 0.93), specificity was 0.90 (95% CI: 0.85 to 0.95), and log diagnostic odds ratio was 4.37 (95% CI: 3.16 to 6.27). Area under the summary ROC curve was 0.86. Positive post-test probability was 79% and negative post-test probability was 6%. In conclusion, this meta-analysis has revealed that caries diagnosis using AI is accurate and its use in clinical practice is justified. Future studies should focus on specific subpopulations, depth of caries, and real-world performance validation to further improve the accuracy of AI in caries diagnosis.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0329986

DOI: 10.1371/journal.pone.0329986

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