Integrating machine learning into acupuncture research: a scoping review
Abstract
Objectives: Machine learning offers new tools to address the variability and subjectivity in acupuncture research. This scoping study aims to map existing literature on the use of machine learning in acupuncture research. It identifies the disease conditions most frequently targeted for machine learning-based efficacy prediction, examines the machine learning methods employed, and assesses the data inputs, methodological limitations, and existing knowledge gaps in the field.
Methods: We conducted a comprehensive literature search in PubMed database using the keywords "acupuncture" and "machine learning" for publications from 2011 to 2024.
Results: A total of 36 relevant articles were identified, with a notable increase after 2019. Most publications originated from China. Seventeen studies focused on predicting acupuncture efficacy, primarily for pain-related conditions. The remaining studies addressed diverse topics, including acupuncture manipulation technique detection, prescription recommendation, exploration of efficacy-related factors, acupoint sensitization prediction and specificity identification, acupuncture usage frequency prediction, investigation of acupoint-meridian conduction effects, and acupuncture robotic point localization. In efficacy prediction studies, support vector machines were the most frequently employed algorithm. Seven of the 11 studies combined Magnetic Resonance Imaging as a feature for their models, and treatment responder classification was…
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