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Background <p>The diagnostic accuracy of caries detection on bitewing radiographs varies among dentists and is strongly influenced by lesion severity. Improving the detection of initial and extensive carious lesions remains clinically important for appropriate treatment planning. This study evaluated the diagnostic performance of a deep learning-based detection model and compared its performance with that of general dentists in detecting caries on bitewing radiographs of primary teeth, applying lesion severity criteria relevant to clinical caries management.</p> Methods <p>A total of 1,427 bitewing radiographs was included, with 1,180 allocated for training and validation, and 247 for testing. As the reference dataset, two experienced dentists annotated carious lesions according to six depth categories based on the International Caries Classification and Management System (ICCMS™). The diagnostic performance of YOLOv8 and the general dentists were compared using recall, precision, F1-score, average precision (AP), and mean average precision (mAP) at an intersection over union (IoU) threshold of 50%.</p> Results <p>YOLOv8 outperformed the general dentists in recall (0.51 vs. 0.29), precision (0.41 vs. 0.31), F1-score (0.44 vs. 0.29), and mAP (0.41 vs. 0.22). Both YOLOv8 and the general dentists demonstrated greater diagnostic accuracy for extensive carious lesions than for initial lesions. Based on the pattern of mispredictions, YOLOv8 tended to underestimate lesion severity, predicting shallower depths than those annotated in the reference dataset, whereas the general dentists exhibited a tendency to overestimate lesion depth.</p> Conclusion <p>The deep learning-based model demonstrated superior performance to the general dentists across all evaluated metrics and lesion classes. These findings support the potential role of deep learning as an adjunctive tool for radiographic caries assessment on bitewing radiographs of primary teeth.</p>

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Deep learning versus general dentists: a clinical evaluation of caries diagnostic accuracy on bitewing radiographs

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月2日

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  • Ploypailin Komtui,
  • Wannakamon Panyarak,
  • Wattanapong Suttapak,
  • Areerat Nirunsittirat,
  • Kittichai Wantanajittikul,
  • Onnida Wattanarat

摘要

Background

The diagnostic accuracy of caries detection on bitewing radiographs varies among dentists and is strongly influenced by lesion severity. Improving the detection of initial and extensive carious lesions remains clinically important for appropriate treatment planning. This study evaluated the diagnostic performance of a deep learning-based detection model and compared its performance with that of general dentists in detecting caries on bitewing radiographs of primary teeth, applying lesion severity criteria relevant to clinical caries management.

Methods

A total of 1,427 bitewing radiographs was included, with 1,180 allocated for training and validation, and 247 for testing. As the reference dataset, two experienced dentists annotated carious lesions according to six depth categories based on the International Caries Classification and Management System (ICCMS™). The diagnostic performance of YOLOv8 and the general dentists were compared using recall, precision, F1-score, average precision (AP), and mean average precision (mAP) at an intersection over union (IoU) threshold of 50%.

Results

YOLOv8 outperformed the general dentists in recall (0.51 vs. 0.29), precision (0.41 vs. 0.31), F1-score (0.44 vs. 0.29), and mAP (0.41 vs. 0.22). Both YOLOv8 and the general dentists demonstrated greater diagnostic accuracy for extensive carious lesions than for initial lesions. Based on the pattern of mispredictions, YOLOv8 tended to underestimate lesion severity, predicting shallower depths than those annotated in the reference dataset, whereas the general dentists exhibited a tendency to overestimate lesion depth.

Conclusion

The deep learning-based model demonstrated superior performance to the general dentists across all evaluated metrics and lesion classes. These findings support the potential role of deep learning as an adjunctive tool for radiographic caries assessment on bitewing radiographs of primary teeth.