LAUSR

Auriculotherapy is one of the main forms of treatment in Traditional Chinese Medicine, whose potential as an alternative medicine for both health evaluation and disease treatment has been reported in many cases. However, its efficacy highly relies on the accurate localization of auricular points, which are not easy to be remembered due to their complexity. To explore an efficient way of locating auricular points, this study proposed a deep learning-based method of automatically locating auricular points from auricular images. A self-collected dataset named EID was created for TCM auriculotherapy research, with 91 auriculotherapy-related landmark points manually annotated according to the Chinese national standardization. A deep neural network structure was trained for landmark detection, and a direction normalization module was proposed to compensate for the detection error caused by the difference between the left and right ears. The trained model was validated on dataset EID. An average NME of 0.0514±0.0023 was achieved, which outperformed similar works. In addition, a certain auricular area corresponding to the digestive system was segmented based on the localized landmarks, and the results were tested in real-time video streaming. The proposed work for both auricular landmark and area identification can be widely used in auriculotherapy education and applications.