AI-Assisted Knee Infrared Imaging Based Acupuncture for Treating Knee Osteoarthritis: A Randomized Controlled Study Protocol
Bibliographic record
Abstract
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 workCorrespondence: Yan Xue, Email joycexy1103@163.com Yuelong Cao, Email ningtcm@126.comBackground: 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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".