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

Journal: Zhongguo zhen jiu = Chinese acupuncture & moxibustion
Published Date: Jan 14, 2025

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.

Authors

  • Yuge Dong
    College of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.
  • Chengbin Wang
    School of Automation, Guangdong University of Technology.
  • Weigang Ma
    College of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.
  • Weifang Gao
    Department of Neurology, Peking Union Medical College Hospital, Beijing, 100730, China.
  • Yuzi Tang
    College of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.
  • Yonglong Zhang
    School of Aerospace Engineering, Tsinghua University, Beijing, China.
  • Jiwen Qiu
    College of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.
  • Haiyan Ren
    1College of Acupuncture-Moxibustion and Tuina, 7College of TCM, Tianjin University of TCM, Tianjin 301617, China; 2Tianjin University of TCM; 3Tianjin Key Laboratory of Innovation and Transformation of Modern TCM Theory, Tianjin 301617; 4Institute of Standardization of TCM, Tianjin University of TCM, Tianjin 301617; 5Tianjin Institute of Wisdom TCM Industry, Tianjin 300193; 6Tianjin Key Laboratory of Intelligent TCM Diagnosis and Treatment Technology and Equipment, Tianjin 301617.
  • Zhongzheng Li
    College of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.
  • Tianyi Zhao
    Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
  • Zhongxi Lv
    School of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.
  • Xingfang Pan
    College of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.

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