AI-Assisted Knee Infrared Imaging Based Acupuncture for Treating Knee: A Comprehensive Overview
Introduction
Knee osteoarthritis (KOA) is a chronic degenerative joint disease characterized by the progressive deterioration of articular cartilage, resulting in pain, stiffness, and functional impairment. Epidemiological studies indicate that the prevalence of KOA increases significantly with age, affecting approximately 10% of men and 13% of women aged 60 years and older. This condition not only imposes significant limitations on physical function but also severely impacts quality of life, increases healthcare utilization, and poses a substantial economic burden.
Acupuncture, a cornerstone of Traditional Chinese Medicine (TCM), has been practiced for centuries and is widely used to alleviate pain and improve functional outcomes in patients with KOA. Its therapeutic mechanism involves the insertion of fine needles into specific acupoints, which are believed to stimulate the body’s natural healing processes and restore energy balance. Numerous studies have demonstrated the efficacy of acupuncture in reducing pain and improving joint function, with proposed mechanisms including the release of endogenous opioids, modulation of inflammatory pathways, and enhancement of local blood circulation. Despite its promising therapeutic effects, the variability in acupoint selection remains a significant limitation to its widespread application.
Traditional Acupoint Selection
Traditional acupoint selection is predominantly based on the practitioner’s experience and TCM theoretical framework, leading to substantial individual variability and a lack of standardization. Evidence suggests that the effectiveness of acupuncture largely depends on the acupoints selected; however, the lack of reproducibility undermines the understanding of the therapeutic potential of acupuncture.
In current acupuncture trials, acupoint selection for KOA can be categorized into local points, distal points, and combined approaches. Local points, such as Liangqiu (ST34), Zusanli (ST36), Neixiyan (EX-LE5), Dubi (ST35), Yinlingquan (SP9), Xuehai (SP10), and Yanglingquan (GB34), target localized symptoms like pain and stiffness around the knee joint. Distal points, such as Quchi (LI11), are selected based on the meridian theory to address systemic symptoms indirectly. Combined approaches integrate local and distal acupoints to treat both localized and systemic symptoms. These methods are commonly based on empirical practice and traditional theories, lacking scientific rigor and broad applicability.
Sensitized Acupoints and Infrared Thermography
According to TCM, acupoints associated with specific conditions may exhibit sensitization, characterized by localized changes such as increased skin temperature, redness, swelling, or tenderness. Sensitized acupoints are thought to reflect underlying pathological changes, providing a potential basis for personalized acupoint selection. In previous work by our research team, infrared thermography was employed to study KOA patients, revealing that localized skin temperature patterns are closely related to clinical symptoms and inflammatory markers. These findings provided an objective foundation for developing individualized acupuncture protocols.
AI-Assisted Acupuncture
In recent years, advancements in artificial intelligence (AI) have created new opportunities in precision medicine. By integrating AI algorithms with infrared imaging technology, it is possible to significantly enhance the standardization and personalization of acupuncture treatments. Portable devices equipped with infrared cameras can rapidly generate personalized acupoint selection protocols based on patients’ thermal profiles, addressing the limitations of traditional methods such as subjectivity and variability. This approach has the potential to offer more consistent and effective treatment options, improving clinical outcomes.
Method and Analysis
Study Design
This study is a multi-center, randomized, sham-controlled, parallel, participant- and assessor-blinded clinical trial. The study will be conducted simultaneously at four hospitals: Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Hainan Branch of Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, and Zhongshan Hospital of Traditional Chinese Medicine. The trial is registered with the Chinese Clinical Trial Registry (ChiCTR2400087106).
Inclusion and Exclusion Criteria
Only patients who meet all of the following criteria will be recruited: (1) Meeting the clinical diagnostic criteria for KOA as defined by the American College of Rheumatology (ACR); (2) Age 50 years or older; (3) Knee pain lasting more than 3 months; (4) NRS pain score of 4 or higher out of 10; (5) Radiologic confirmation of KOA, classified as Kellgren-Lawrence grade II or III. Patients meeting any of the following criteria will not be eligible for inclusion: (1) History of systemic arthritis; (2) Have had knee surgery or are awaiting knee surgery; (3) Any other condition that affects the function of the lower extremities (eg, trauma, malignant tumors, neurological disorders); (4) Have received any knee injections (eg, adrenocorticotropic hormone, hyaluronic acid) within the past 6 months; (5) Current use of oral or injectable anticoagulant medications; (6) Used acupuncture therapy within the past 3 months; (7) Have any bleeding disorders. (8) Referred to a pain clinic or use of morphine or pethidine within the last 6 months; (9) Any other medical condition that makes them unsuitable for participation in a clinical trial (eg, kidney or liver disease, deep vein thrombosis); (10) Unable to provide written informed consent.
Random Allocation and Blinding
Eligible participants will be randomly assigned to three groups in a 1:1:1 ratio. An independent researcher, who is not involved in the trial, will generate the randomization sequence using SAS version 9.4, stratified by enrolling hospitals with random block sizes of 6. The randomization scheme is concealed using opaque envelopes, each labeled with a unique code and sealed before being handed over to the researchers for safekeeping. After a participant has met all selection criteria, signed the informed consent form, and completed the baseline assessments, the evaluator informs the acupuncturists. The acupuncturists will open the envelope according to the participant’s screening sequence number and then assign the participant to one of the three groups: the AI-assisted personalized acupoint group, the conventional acupuncture group, and the sham acupuncture group.
Interventions
This trial is divided into three groups: the specific acupoint group, the conventional acupuncture group, and the sham acupuncture group, receiving acupuncture based on AI-assisted infrared image-based acupoints, conventional acupoints, or sham acupoints, respectively. To ensure data consistency across all four participating centers, all infrared imaging devices will undergo standardized calibration before the initiation of the trial and regular recalibration every three months, following the manufacturer’s technical specifications. Imaging sessions will be conducted in temperature-controlled rooms maintained at 24±1°C and a relative humidity of 45–60%, without direct sunlight or strong airflow. Prior to image acquisition, participants will rest quietly in the imaging room for at least 15 minutes to achieve thermal equilibrium. All centers will use the same infrared imaging device model and follow a unified operating protocol to minimize inter-site variability and ensure data comparability.
Outcome Measures
Knee function and pain are defined as co-primary outcomes in this study. Functional status was assessed using the function subscale of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC, Likert 3.1 version), and knee pain was evaluated using the Numeric Rating Scale (NRS). The WOMAC function subscale includes 17 items assessing difficulty in performing daily activities. Each item is scored on a 5-point Likert scale: none (0), mild (1), moderate (2), severe (3), and extreme (4), with total scores ranging from 0 to 68. Higher scores indicate worse knee function. For patients with bilateral knee involvement, the more severely affected side was used for scoring. Pain was assessed based on average knee pain experienced over the past week, using an 11-point NRS ranging from 0 (no pain) to 10 (worst imaginable pain).
Discussion
This study aims to evaluate the effectiveness of AI-assisted acupoint selection based on infrared imaging compared with traditional experience-based methods for treating KOA, laying the groundwork for the standardization and optimization of acupuncture practices and paving the way for broader clinical applications.
