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

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 external 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 (AP@0.5), F1 score, and accuracy 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@0.5:0.95 was 0.51 for TW-YOLO and 0.45 for YOLOv5. Explainability analysis suggested that the TW-YOLO model could implicitly learn the distribution pattern of WSLs by shifting more attention toward these regions. Conclusion : The novel TW-YOLO model demonstrated not only improved accuracy but also the potential to be applied in other related dentistry studies.