What is it about?
The efficacy of acupoints in Chinese medicine has been demonstrated in many medical protocols, such as acupuncture and tuina. The theory of acupoint reflex visualization suggests that local parts of the body (e.g., the hand) can reflect the organs of the whole body, which are known as acupoint reflex zones. We propose a deep learning method to learn such acupoint reflex zone patterns from a large number of labeled hand reflex zone images.
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Why is it important?
The systematic training of a Traditional Chinese Medicine (TCM) practitioner takes a long time. Professional TCM practitioners often have a large number of outpatient and clinical needs. Our proposed deep learning-based acupoint reflex zone segmentation method, based on a large number of image segmentation datasets produced from the experience of professional TCM practitioners, can automatically segment the corresponding acupoint reflex zones from different palms, which can significantly assist TCM practitioners in diagnosis and treatment and reduce the workload of TCM practitioners. Meanwhile, TCM is often taught on an apprenticeship basis. It is also a good way for beginners in TCM to learn on their own.
Perspectives
I hope that this article will make the seemingly arcane field of Traditional Chinese Medicine (TCM) healing interesting to people. With the rise of deep learning, many modern medicines, especially medical imaging, are closely integrated with deep learning algorithms. I have been working on deep learning algorithms for more than ten years. I hope to use my skills to contribute to the promotion of Chinese medicine. At the same time, I hope this article can make more people understand Chinese medicine from the perspective of data science.
Yi Zheng
Jianghan University
Read the Original
This page is a summary of: Hybrid Attention-based Semantic Segmentation for Hand Acupoint Reflex Zones, October 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3644116.3644298.
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