Dong Yuge, Wang Chengbin, Ma Weigang, Gao Weifang, Tang Yuzi, Zhang Yonglong, Qiu Jiwen, Ren Haiyan, Li Zhongzheng, Zhao Tianyi, Lv Zhongxi, Pan Xingfang
School of Acupuncture-Moxibustion and Tuina, Tianjin University of TCM, Tianjin 301617, China; Experimental Acupuncture Research Center, Tianjin University of TCM, Tianjin 301617.
School of Automation, Guangdong University of Technology.
Zhongguo Zhen Jiu. 2025 May 12;45(5):586-592. doi: 10.13703/j.0255-2930.20241002-k0001. Epub 2025 Jan 14.
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.
本文综述了近年来发表的关于深度学习方法在穴位自动定位中的应用的文章,并从数据集构建、神经网络模型设计和穴位定位准确性评估这3个关键环节进行了总结。深度学习在穴位定位领域已取得显著进展,但穴位检测的规模有待扩大,模型的精度、泛化能力和实时性能也有待提高。未来的研究应聚焦于标准化数据集的支持以及三维建模与多模态数据融合的整合,以提高穴位定位的准确性并增强其个性化。