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Background <p>To develop a new deep learning model for detecting white spot lesions (WSLs), which are commonly observed in patients undergoing orthodontic treatment, and assess its accuracy.</p> Methods <p>A total of 653 intra-oral photographs of WSLs were collected and annotated. Our novel model, tooth-to-WSL You Only Look Once (TW-YOLO), and the original YOLOv5 model were fine-tuned and evaluated, with 457 photographs used for training; 130, for validation; and 66, for hold-out testing. Cohen's kappa coefficient between model prediction and orthodontist annotation was used as the primary evaluation metric, and mean average precision (mAP@0.5:0.95), average precision (mAP@0.5) and F1 score were also evaluated. The score-CAM technique was used for explainability analysis.</p> Results <p>Cohen's kappa coefficient values were 0.76 and 0.62 for TW-YOLO and YOLOv5, respectively. The mAP@0.5 and mAP@0.5:0.95 were 0.78, 0.51 for TW-YOLO and 0.69, 0.45 for YOLOv5, respectively. Explainability analysis suggested that the TW-YOLO model could implicitly learn the distribution pattern of WSLs by shifting more attention toward these regions.</p> Conclusion <p>Compared to original YOLO model, our novel TW-YOLO model demonstrated improved accuracy. Smaller proportion of small sized object and examine tooth enamel at original resolution contributed to this improvement.</p>

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Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月9日

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  • Hau Man Chung,
  • Jingjing Ke,
  • Mengdan Zhang,
  • Lixian Kong,
  • Junming Zheng,
  • Lusai Xiang

摘要

Background

To develop a new deep learning model for detecting white spot lesions (WSLs), which are commonly observed in patients undergoing orthodontic treatment, and assess its accuracy.

Methods

A total of 653 intra-oral photographs of WSLs were collected and annotated. Our novel model, tooth-to-WSL You Only Look Once (TW-YOLO), and the original YOLOv5 model were fine-tuned and evaluated, with 457 photographs used for training; 130, for validation; and 66, for hold-out testing. Cohen's kappa coefficient between model prediction and orthodontist annotation was used as the primary evaluation metric, and mean average precision (mAP@0.5:0.95), average precision (mAP@0.5) and F1 score were also evaluated. The score-CAM technique was used for explainability analysis.

Results

Cohen's kappa coefficient values were 0.76 and 0.62 for TW-YOLO and YOLOv5, respectively. The mAP@0.5 and mAP@0.5:0.95 were 0.78, 0.51 for TW-YOLO and 0.69, 0.45 for YOLOv5, respectively. Explainability analysis suggested that the TW-YOLO model could implicitly learn the distribution pattern of WSLs by shifting more attention toward these regions.

Conclusion

Compared to original YOLO model, our novel TW-YOLO model demonstrated improved accuracy. Smaller proportion of small sized object and examine tooth enamel at original resolution contributed to this improvement.