Identification of auricular acupoints using a convolutional neural network.

Background: The accurate identification of acupoints is an essential task in acupuncture therapy. Recent advancements in artificial intelligence (AI) have led to the exploration of automated landmark detection systems, which may provide more accurate and reliable acupoint detection. This study investigated the efficiency of an AI model in predicting the shenmen, lung, and mouth auricular acupoints and compared its performance to placements made by a practitioner of traditional Korean medicine. Methods: Ear images from 39 individuals were captured from three different angles. The mask region-based convolutional neural network (Mask R-CNN) model was utilized to isolate the ear region, followed by landmark detection using a CNN model trained on resized images to predict three auricular acupoints. Model reliability was enhanced by treating each acupoint as a separate prediction coordinate. Acupoint distribution was also estimated using a kernel density estimation method. Results: Centroids of auricular acupoints predicted by the CNN model showed deviations of < 3 pixels from traditional placements by the practitioner. Kernel density estimation showed that CNN predictions led to narrower acupoint distributions compared with those placed by the practitioner, suggesting higher consistency in CNN model predictions across different images. Conclusions: The AI-driven approach showed significant potential in improving both the accuracy and consistency of auricular acupoint identification. These findings support the integration of AI into acupuncture practice as a reliable tool for enhancing clinical accuracy and precision of acupoint location.