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Evaluating YOLO for dental caries diagnosis: a systematic review and meta-analysis

Evidence-Based Dentistry (2025)Cite this article

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Abstract

Objectives

Recent advancements in the You Only Look Once (YOLO) algorithm show promise for dental caries diagnosis. We aimed to evaluate the diagnostic performance of different YOLO versions using photographic and radiographic images for caries detection.

Methods

We searched PubMed (MEDLINE), EMBASE, Web of Science, and Scopus for studies up to December 12, 2024. Studies using any YOLO version for caries detection were included. Binary diagnostic accuracy data were extracted to calculate pooled sensitivity, specificity, and area under the curve (AUC) using a bivariate random-effects model. Quality was assessed with QUADAS-2 and the Radiomics Quality Score (RQS). This review is registered in PROSPERO (CRD42024615440).

Results

We included 15 studies in the systematic review and 14 in the meta-analysis. Overall, YOLO-based models achieved a pooled sensitivity of 79.3% and specificity of 84.9%, with an AUC of 0.832. YOLO using radiographic images demonstrated higher specificity (92.5% vs 72.0%) and AUC (0.847 vs 0.735) than using photographic images, while sensitivity was similar (78.6% vs 80.0%). Differences between YOLO versions (v5 and earlier vs v6 and later) and the use of external validation did not significantly affect diagnostic accuracy.

Discussion

Radiograph-based YOLO models showed superior specificity to photograph-based models, reflecting the higher diagnostic detail of radiographs. However, photographic approaches are completely radiation-free and more accessible, which could benefit screening in low-resource settings. Newer YOLO versions did not significantly outperform older versions, likely due to the limited complexity of the task and dataset constraints in current studies.

Conclusions

YOLO algorithms provide a reliable tool for dental caries detection. Radiograph imaging combined with YOLO offers enhanced diagnostic specificity, while even older YOLO versions remain effective for caries detection in practice.

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Fig. 1
Fig. 2: Quality assessments of included studies using the Radiomics Quality Score (RQS) and QUADAS-2 tool.
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Data availability

All data analyzed in this study were obtained from previously published articles (the studies included in this systematic review and meta-analysis). The extracted datasets supporting the conclusions of this article are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank the support of their respective institutions. The authors also thank colleagues for helpful discussions and feedback during the preparation of this manuscript.

Funding

No specific grant from any funding agency in the public, commercial, or not-for-profit sectors was received for this research.

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Authors and Affiliations

  1. School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan

    Quang Tuan Lam & I-Ta Lee

  2. AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan

    Quang Tuan Lam, Minh Huu Nhat Le & Nguyen Quoc Khanh Le

  3. Faculty of Odonto-Stomatology, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam

    Quang Tuan Lam

  4. International Ph.D. Program in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

    Minh Huu Nhat Le

  5. In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan

    Nguyen Quoc Khanh Le

  6. Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan

    Nguyen Quoc Khanh Le

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  2. Minh Huu Nhat Le
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Contributions

Q.T.L. conceived and designed the study. Q.T.L. and M.H.N.L. performed the literature search and screening. Q.T.L. extracted the data and conducted the meta-analysis, while M.H.N.L. cross-checked the data and performed the quality assessments. N.Q.K.L. and I.-T.L. provided methodological guidance and helped resolve any discrepancies during the review process. All authors contributed to the interpretation of results. Q.T.L. drafted the manuscript, and M.H.N.L., N.Q.K.L. and I.-T.L. critically revised it. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to I-Ta Lee or Nguyen Quoc Khanh Le.

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Lam, Q.T., Le, M.H.N., Lee, IT. et al. Evaluating YOLO for dental caries diagnosis: a systematic review and meta-analysis. Evid Based Dent (2025). https://doi.org/10.1038/s41432-025-01180-1

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