Year: 2025
Type: article
Abstract: 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 ([email protected]:0.95), average precision ([email protected]) 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 [email protected] and [email protected]: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.
Source: BMC Oral Health
Authors: Hau Man Chung, Jingjing Ke, Mengdan Zhang, Lixian Kong, Junming Zheng, Lusai Xiang
Language: English
Subfield: Oral Surgery
Field: Dentistry
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,190
Award: G7212751120
OpenAlex ID: W4414984926