Abstract: Objective: To systematically evaluate the performance and feasibility of the YOLOv7 model in identifying dental plaque from intraoral photographs taken by fixed orthodontic patients using their smartphones. Methods: The study collected intraoral plaque staining images from 23 patients undergoing fixed orthodontic treatment at the Orthodontics Department of Jilin University Stomatological Hospital between March 2024 and June 2025. They were categorized into three sets: training, validation, and test sets. A GPU-equipped computer was used, with Python, PyTorch, Torchvision, and the other necessary software packages installed to configure the environment for YOLOv7. The model was trained on the training set, validated on the validation set, and tested on the test set to determine the optimal learning weights, enabling the algorithm to identify plaque in intraoral photographs of orthodontic patients. Results: Experimental results showed that the model achieved a plaque identification precision of 82.91% for patients undergoing fixed orthodontic treatment, a Recall of 78.00%, and a mAP50 of 79.60%. The model’s detection success rate outperformed the visual identification of plaque images by both patients and orthodontists. Conclusion: The deep learning algorithm based on YOLOv7 achieved a level suitable for clinical application. This study enables fixed orthodontic patients to obtain real-time plaque detection results simply by taking intraoral photos with a smartphone. In the future, integrating the model into software will significantly enhance the feasibility and effectiveness of daily oral hygiene management for fixed orthodontic patients.

Key words: plaque detection, orthodontics, oral health, oral health care, deep learning