A review of acupoint localization based on deep learning
I tiakina i:
| I whakaputaina i: | Chinese Medicine vol. 20 (2025), p. 1-30 |
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| Kaituhi matua: | |
| Ētahi atu kaituhi: | , , , , |
| I whakaputaina: |
Springer Nature B.V.
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| Ngā marau: | |
| Urunga tuihono: | Citation/Abstract Full Text Full Text - PDF |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| Whakarāpopotonga: | 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. |
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| ISSN: | 1749-8546 |
| DOI: | 10.1186/s13020-025-01173-3 |
| Puna: | Health & Medical Collection |