Exploring an Innovative Deep Learning Solution for Acupuncture Point Localization on the Weak Feature Body Surface of the Human Back

• Published in: IEEE journal of biomedical and health informatics Vol. 29; no. 7; pp. 4599 - 4611
• Main Authors: Yang, Shilong, Li, Yalan, Zou, Hao, Huang, Lingfeng, Liu, Jing, Teng, Yongsheng, Xie, Yaoqin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2025
• Subjects: Accuracy; Acupuncture; Acupuncture point localization; Acupuncture Points; Adult; artificial intelligence; Classification algorithms; Convolutional neural networks; Deep Learning; Feature extraction; Female; Humans; Image Processing, Computer-Assisted - methods; key points prediction; Location awareness; Male; Medical diagnostic imaging; Medicine, Chinese Traditional; Neural Networks, Computer; Target recognition; traditional chinese medicine; transformer; Visualization
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2024.3511128

In current clinical practice, the localization of human acupuncture points relies extensively on the subjective experience of physicians. Therefore, despite being a crucial basic content of traditional Chinese medicine (TCM), acupuncture point localization has not been well expanded and promoted through intelligent means. Our goal is to explore an efficient and reliable solution for acupuncture point localization and recognition that addresses the shortcomings of subjectivity and standardization in this task. We focus on the weak feature body surface of the human back and propose an innovative approach that utilizes a deep learning network with a self-attention module for global extraction of image features. This methodology differs from common Convolutional Neural Networks (CNNs) which often lead to classification ambiguous in weak feature image tasks due to excessive cropping and scaling operations during feature extraction. Moreover, our self-constructed dataset of human back acupuncture points provides data support for model training. The localization task for the back acupuncture points of the subjects in the dataset strictly follows the national standard definition and is labelled by professional doctors of TCM to ensure data robustness and quality. Our preliminary experiments validate that our proposed network learns higher-quality global image features, achieving an average accuracy of less than 1cm in the localization and recognition task of 84 acupuncture points on the back of the human body.