Analysis of Acupoint Selection Rules for Guasha Treatment of Primary Headaches Based on Data Mining

Objective

We aimed to understand the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches using data mining technology, providing a reference for the clinical application of Guasha therapy for primary headaches.

Methods

Literature related to Guasha therapy for primary headaches in PubMed, Web of Science, Chinese National Knowledge Infrastructure, Wanfang Data Knowledge Service Platform, and Chinese Biomedical Literature Database were searched, up until May 12, 2023. A database of acupoints for Guasha therapy for primary headaches was established in Excel. The frequency of the acupoints used for Guasha in therapy of primary headaches were calculated by SPSS 25.0. The association rules between the acupoints were further described using SPSS Modeler 18.0.

Results

A total of 67 papers were included, involving 51 acupoints for Guasha against primary headaches. The most commonly used acupoints were Fengchi, Baihui, Taiyang, Shuaigu, Tianzhu, and Hegu. The common acupoint combinations for Guasha therapy for primary headaches were Fengchi-Taiyang, Fengchi-Baihui, Fengchi-Taiyang-Baihui, Fengchi-Tianzhu-Baihui, and Fengchi-Shuaigu-Taiyang-Baihui.

Conclusion

Data mining can effectively analyze the commonly used acupoints and the acupoint combination rules in Guasha therapy for primary headaches, providing a reliable basis for clinical acupoint selection.

Artificial intelligence-directed acupuncture: a review

Acupuncture is widely used around the whole world nowadays and exhibits significant efficacy against many chronic diseases, especially in pain-related diseases. With the rapid development of artificial intelligence (AI), its implementation into acupuncture has achieved a series of significant breakthroughs in many areas of acupuncture practice, such as acupoints selection and prescription, acupuncture manipulation identification, acupuncture efficacy prediction, and so on. The paper will discuss the significant theoretical and technical achievements in AI-directed acupuncture. AI-based data mining methods uncovered crucial acupoint combinations for treating various diseases, which provide a scientific basis for acupoints prescription in clinical practice. Furthermore, the rapid development of modern TCM instruments facilitates the integration of modern medical instruments, AI techniques, and acupuncture. This integration significantly improves the quantification, objectification, and standardization of acupuncture as well as the delivery of clinical personalized acupuncture therapy. Machine learning-based clinical efficacy prediction of acupuncture can help doctors screen patients who may benefit from acupuncture treatment. However, the existing challenges require additional work for developing AI-directed acupuncture. Some include a better understanding of ancient Chinese philosophy for AI researchers, TCM acupuncture theory-based explanation of the knowledge discoveries, construction of acupuncture databases, and clinical trials for novel knowledge validation. This review aims to summarize the major contribution of AI techniques to the discovery of novel acupuncture knowledge, the improvement for acupuncture safety and efficacy, the development and inheritance of acupuncture, and the major challenges for the further development of AI-directed acupuncture. The development of acupuncture can progress with the help of AI.