To develop an artificial intelligence-based deep learning model for early screening and detection of periodontal diseases using periodontal images captured by a portable oral endoscope. A dataset consisting of 811 clinical oral periodontal images collected via endoscopy was constructed. After data cleaning and high-quality annotation, it was divided into 271 healthy and 540 unhealthy periodontal images, with an 8:2 split for training and validation. The YOLOv11 deep learning model was used for analysis and early detection of periodontal diseases. Model performance was evaluated using precision, recall, and mean average precision. In the validation set, for binary classification the model achieved a precision of 0.727, a recall rate of 0.583, and a mean average precision of 0.632 at an IOU (Intersection over Union) threshold of 0.5. For multiclass detection, the model’s precision for identifying gingival recession was 0.671, for gingival swollen was 0.609, and for detecting dental calculus was 0.573. The developed deep learning model enables low-cost early screening and detection of periodontal diseases, providing an important direction for the application of artificial intelligence in early periodontal disease screening.
Zhang et al. (Mon,) studied this question.