Research status of automatic localization of acupoint based on deep learning.

Yuge DONG; Chengbin WANG; Weigang MA; Weifang GAO; Yuzi TANG; Yonglong ZHANG; Jiwen QIU; Haiyan REN; Zhongzheng LI; Tianyi ZHAO; Zhongxi LV; Xingfang PAN

Return

Research status of automatic localization of acupoint based on deep learning.

Author: Yuge DONG ¹ ; Chengbin WANG ² ; Weigang MA ¹ ; Weifang GAO ¹ ; Yuzi TANG ¹ ; Yonglong ZHANG ¹ ; Jiwen QIU ¹ ; Haiyan REN ³ ; Zhongzheng LI ¹ ; Tianyi ZHAO ⁴ ; Zhongxi LV ¹ ; Xingfang PAN ¹
Author Information

1. School of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin
2. School of Automation, Guangdong University of Technology.
3. School of TCM, Tianjin University of TCM.
4. Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617; School of TCM, Tianjin University of TCM.
Publication Type:English Abstract
Keywords: acupuncture; artificial intelligence; automatic localization of acupoint; deep learning; intelligent TCM equipment; intelligent acupuncture robot
MeSH: Deep Learning; Acupuncture Points; Humans; Neural Networks, Computer
From: Chinese Acupuncture & Moxibustion 2025;45(5):586-592
CountryChina
Language:Chinese
Abstract: This paper reviews the published articles of recent years on the application of deep learning methods in automatic localization of acupoint, and summarizes it from 3 key links, i.e. the dataset construction, the neural network model design, and the accuracy evaluation of acupoint localization. The significant progress has been obtained in the field of deep learning for acupoint localization, but the scale of acupoint detection needs to be expanded and the precision, the generalization ability, and the real-time performance of the model be advanced. The future research should focus on the support of standardized datasets, and the integration of 3D modeling and multimodal data fusion, so as to increase the accuracy and strengthen the personalization of acupoint localization.