Research on Intelligent Screening of Periodontal Diseases Based on YOLOv11

doi:10.13701/j.cnki.kqyxyj.2026.09.004

Abstract

Abstract: Objective: To develop a deep learning-based AI model for early screening and detection of periodontal diseases using images from a portable oral endoscope. Methods: A dataset of 1206 periodontal disease images captured by the endoscope was constructed. After data curation, cleaning, and standardized annotation, the dataset was split into training, validation, and test sets in a 7∶1∶1 ratio. The YOLOv11 model was employed for training and evaluation, with performance assessed using precision, recall, F1-score, and mean average precision (mAP). Results: On the test set, the model achieved an mAP of 0.8044 at an IoU threshold of 0.5 and an F1-score of 0.7953. The average precision for detecting gingival recession, gingival redness and swelling, and dental calculus was 0.7861, 0.8511, and 0.7761, respectively. Conclusion: The developed deep learning model enables low-cost early screening of periodontal diseases and provides a valuable reference for the application of AI in early periodontal disease detection.

Key words: artificial intelligence, periodontal disease, intraoral endoscopy, deep learning

ZHANG Ting, JIN Nenghao, LI Liangbo, ZHU Liang, LI Zhuangzhuang, ZHANG Haizhong. Research on Intelligent Screening of Periodontal Diseases Based on YOLOv11[J]. Journal of Oral Science Research, 2026, 42(9): 752-757.

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