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已发表论文
基于人工智能辅助膝关节红外成像的针灸治疗膝骨关节炎：一项随机对照研究方案
Authors Yang M, Qiu F, Xie X, Tao L, Zheng W, Wu Y, Xu Z, Xue Y , Cao Y
Received 15 August 2025
Accepted for publication 5 November 2025
Published 19 November 2025 Volume 2025:18 Pages 6181—6195
DOI https://doi.org/10.2147/JPR.S560805
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Houman Danesh
Muyun Yang,1,* Fengxi Qiu,2,* Xianfei Xie,3 Lin Tao,3 Weihong Zheng,4 Yufeng Wu,4 Zhaohong Xu,5 Yan Xue,2 Yuelong Cao1
1Characteristic Diagnosis and Treatment Technology Research Institution, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Department of Traditional Chinese Medicine, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Shanghai, People’s Republic of China; 3Department of Orthopedics Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, People’s Republic of China; 4Department of Orthopedics, Zhongshan Hospital of Traditional Chinese Medicine, Guangdong, People’s Republic of China; 5School of Artificial Intelligence and Application, Shanghai Urban Construction Vocational College, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yan Xue, Email joycexy1103@163.com Yuelong Cao, Email ningtcm@126.com
Background: The variability in acupoint selection limits the standardization of acupuncture for knee osteoarthritis (KOA) and is one of the important factors affecting treatment efficacy. Recent advancements in artificial intelligence (AI) and infrared imaging provide opportunities to enhance the precision and standardization of acupuncture.
Methods: This multicenter, single-blind, randomized controlled trial aims to evaluate whether AI-assisted personalized acupuncture is superior to traditional acupuncture and sham acupuncture in alleviating pain and improving joint function in patients with KOA. A total of 120 participants will be recruited from four hospitals in China and randomly assigned to three groups: the specific acupoint group (n=40), the conventional acupoint group (n=40), and the sham acupuncture group (n=40). All groups will receive acupuncture treatment twice a week for 8 weeks, with a total of 16 sessions. Outcome assessments will be conducted at baseline, week 8, and week 12. The AI system utilizes infrared imaging to identify heat-sensitive knee surface areas, and generates individualized acupoint prescriptions through internal decision analysis.
Discussion: The primary outcomes are knee pain (Numeric Rating Scale, NRS) and function (WOMAC subscale). Secondary outcomes include knee pain and stiffness (Western Ontario and McMaster Universities Osteoarthritis Index subscale, WOMAC subscale), quality of life (Short Form 12, SF-12), knee range of motion, Traditional Chinese Medicine (TCM) clinical efficacy, and inflammatory indicators (IL-1β, IL-6, and TNF-α). This trial is expected to provide high-quality evidence for the clinical value and standardization of AI-assisted acupuncture.
Trial Registration: This study has been registered with the Chinese Clinical Trial Registry (ChiCTR2400087106, July 19, 2024).
Keywords: knee osteoarthritis, koa, acupuncture, artificial intelligence, AI, infrared imaging
1Characteristic Diagnosis and Treatment Technology Research Institution, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of China; 2Department of Traditional Chinese Medicine, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Shanghai, People’s Republic of China; 3Department of Orthopedics Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, People’s Republic of China; 4Department of Orthopedics, Zhongshan Hospital of Traditional Chinese Medicine, Guangdong, People’s Republic of China; 5School of Artificial Intelligence and Application, Shanghai Urban Construction Vocational College, Shanghai, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Yan Xue, Email joycexy1103@163.com Yuelong Cao, Email ningtcm@126.com
Background: The variability in acupoint selection limits the standardization of acupuncture for knee osteoarthritis (KOA) and is one of the important factors affecting treatment efficacy. Recent advancements in artificial intelligence (AI) and infrared imaging provide opportunities to enhance the precision and standardization of acupuncture.
Methods: This multicenter, single-blind, randomized controlled trial aims to evaluate whether AI-assisted personalized acupuncture is superior to traditional acupuncture and sham acupuncture in alleviating pain and improving joint function in patients with KOA. A total of 120 participants will be recruited from four hospitals in China and randomly assigned to three groups: the specific acupoint group (n=40), the conventional acupoint group (n=40), and the sham acupuncture group (n=40). All groups will receive acupuncture treatment twice a week for 8 weeks, with a total of 16 sessions. Outcome assessments will be conducted at baseline, week 8, and week 12. The AI system utilizes infrared imaging to identify heat-sensitive knee surface areas, and generates individualized acupoint prescriptions through internal decision analysis.
Discussion: The primary outcomes are knee pain (Numeric Rating Scale, NRS) and function (WOMAC subscale). Secondary outcomes include knee pain and stiffness (Western Ontario and McMaster Universities Osteoarthritis Index subscale, WOMAC subscale), quality of life (Short Form 12, SF-12), knee range of motion, Traditional Chinese Medicine (TCM) clinical efficacy, and inflammatory indicators (IL-1β, IL-6, and TNF-α). This trial is expected to provide high-quality evidence for the clinical value and standardization of AI-assisted acupuncture.
Trial Registration: This study has been registered with the Chinese Clinical Trial Registry (ChiCTR2400087106, July 19, 2024).
Keywords: knee osteoarthritis, koa, acupuncture, artificial intelligence, AI, infrared imaging