Identification of auricular acupoints using a convolutional neural network
The AI-driven approach showed significant potential in improving both the accuracy and consistency of auricular acupoint identification. These findings support the integration of AI into acupuncture practice as a reliable tool for enhancing clinical accuracy and precision of acupoint location.
Authors
- In‐Seon Lee (ORCID: https://orcid.org/0000-0001-9275-3111)
- Da-Eun Yoon (ORCID: https://orcid.org/0000-0002-6718-273X)
- Younbyoung Chae (ORCID: https://orcid.org/0000-0001-6787-2215)
- Junsuk Kim
- Young-Seok Kim (ORCID: https://orcid.org/0000-0001-9984-2416)
Institutions
- Kwangwoon University (KR)
- Kyung Hee University (KR)
Publication Details
- Journal
- Integrative Medicine Research
- Published
- 2025-08-18
- DOI
- https://doi.org/10.1016/j.imr.2025.101226
- Citations
- 1
- Primary Topic
- Acupuncture Treatment Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.38