A review of acupoint localization based on deep learning

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Xuất bản năm:	Chinese Medicine vol. 20 (2025), p. 1-30
Tác giả chính:	Li, Jiahao
Tác giả khác:	Zhennan Fei, Xie, Yingjiang, Deng, Da, Xingcheng Ming, Niu, Fu
Được phát hành:	Springer Nature B.V.
Những chủ đề:	Machine learning Acupuncture Software Accuracy Traditional Chinese medicine Deep learning Artificial intelligence Algorithms Neural networks Localization
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Miêu tả
Bài tóm tắt:	The development of deep learning has brought unprecedented opportunities for automatic acupoint localization, surmounting many limitations of traditional methods and machine learning, and significantly propelling the modernization of Traditional Chinese Medicine (TCM). We comprehensively review and analyze relevant research in this field in recent years, and examine the principles, classifications, commonly used datasets, evaluation metrics and application fields of acupoint localization algorithms based on deep learning. We categorize them by body part, algorithm architecture, localization strategy, and image modality, and summarize their characteristics, pros and cons, and suitable application scenarios. Then we sieve out representative datasets of high value and wide application, and detail some key evaluation metrics for better assessment. Finally, we sum up the application status of current automatic acupoint localization technology in various fields, hoping to offer practical reference and guidance for future research and practice.
số ISSN:	1749-8546
DOI:	10.1186/s13020-025-01173-3
Nguồn:	Health & Medical Collection