A YOLOv11-Based AI System for Keypoint Detection of Auricular Acupuncture Points in Traditional

Ganhong Wang¹Limei Yin¹Hanyue Zhang¹Kaijian Xia²Yue Su^1*Jian Chen^2*

• ¹Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, Changshu City, China
• ²Changshu Hospital Affiliated to Soochow University, Changshu City, China

Objective This study aims to develop an artificial intelligence model and web-based application for the automatic detection of 21 commonly used auricular acupoints based on the YOLOv11 neural network.Methods A total of 660 human ear images were collected from three medical centers. The LabelMe annotation tool was used to label the images with bounding boxes and key points, which were then converted into a format compatible with the YOLO model. Using this dataset, transfer learning and fine-tuning were performed on different-sized versions of the YOLOv11 neural network. The model performance was evaluated on validation and test sets, considering metrics such as mean average precision (mAP) under different thresholds, recall, and detection speed. The best-performing model was subsequently deployed as a web application using the Streamlit library in the Python environment.Five versions of the YOLOv11 keypoint detection model were developed, namely YOLOv11n, YOLOv11s, YOLOv11m, YOLOv11l, and YOLOv11x. Among them, YOLOv11x achieved the highest performance in the validation set with a precision of 0.991, recall of 0.976, mAP 50 of 0.983, and mAP 50-95 of 0.625, though it exhibited the longest inference delay (19ms/img). On the external test set, YOLOv11x achieved an ear recognition accuracy of 0.996, sensitivity of 0.996, and an F1-score of 0.998. For auricular acupoint localization, the model achieved an mAP 50 of 0.982, precision of 0.975, and recall of 0.976. The model has been successfully deployed as a web application, accessible on both mobile and desktop platforms to accommodate diverse user needs.The YoloEar21 web application, developed based on YOLOv11x and Streamlit, demonstrates superior recognition performance and user-friendly accessibility. Capable of providing automatic identification of 21 commonly used auricular acupoints across various scenarios for diverse users, it exhibits promising potential for clinical applications.