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

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.

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).

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.

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.

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.