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Acupoint Needle-Embedding Combined with Ironing Therapy for Postoperative Pain After Anal Surgery

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A systematic review of computer-aided acupoint localization.

Zhenwei Zhai¹, Zihao Wang², Lin Xu³

¹School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.

|November 10, 2025

Summary

Automated acupoint localization using artificial intelligence (AI) and deep learning shows promise for improving traditional acupuncture. This review highlights current methods, limitations, and future directions for AI-driven acupoint identification.

Keywords:

computer sciences health sciences

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Area of Science:

Integrative Medicine
Medical Technology
Artificial Intelligence

Background:

Acupuncture is a globally applied traditional therapy.
Traditional acupoint localization relies on subjective clinical experience.
Advancements in AI, particularly deep learning, are enabling automated approaches.

Purpose of the Study:

To systematically review literature on acupoint localization using traditional algorithms and deep learning.
To evaluate the effectiveness and limitations of automated acupoint localization techniques.
To identify future research directions in AI-based acupoint identification.

Main Methods:

Systematic literature review of studies on acupoint localization.
Analysis of traditional algorithms and deep learning methods.
Evaluation of techniques across different anatomical regions.

Main Results:

Current AI and deep learning methods show potential for automated acupoint localization.
Limitations in existing techniques require further research and development.
The review identifies key challenges and areas for future investigation.

Conclusions:

Automated acupoint localization techniques are expected to improve with further AI advancements.
Overcoming current challenges will lead to more promising outcomes in AI-driven acupoint identification.
This review encourages further exploration of advanced AI methods for acupoint localization.