Background: Accurate preoperative planning for total knee arthroplasty (TKA) is crucial. Computed tomography (CT)-based preoperative planning offers more comprehensive information and can also be used to design patient-specific instrumentation (PSI), but it requires well-reconstructed and segmented images, and the process is complex and time-consuming. This study aimed to develop an artificial intelligence (AI) preoperative planning and PSI system for TKA and to validate its time savings and accuracy in clinical applications. Methods: The 3D-UNet and modified HRNet neural network structures were used to develop the AI preoperative planning and PSI system (AIJOINT). Forty-two patients who were scheduled for TKA underwent both AI and manual CT processing and planning for component sizing, 20 of whom had their PSIs designed and applied intraoperatively. The time consumed and the size and orientation of the postoperative component were recorded. Results: The Dice similarity coefficient (DSC) and loss function indicated excellent performance of the neural network structure in CT image segmentation. AIJOINT was faster than conventional methods for CT segmentation (3.74 ± 0.82 vs. 128.88 ± 17.31 min, p < 0.05) and PSI design (35.10 ± 3.98 vs. 159.52 ± 17.14 min, p < 0.05) without increasing the time for size planning. The accuracy of AIJOINT in planning the size of both femoral and tibial components was 92.9%, while the accuracy of the conventional method in planning the size of the femoral and tibial components was 42.9% and 47.6%, respectively (p < 0.05). In addition, AI-based PSI improved the accuracy of the hip–knee–ankle angle and reduced postoperative blood loss (p < 0.05). Conclusion: AIJOINT significantly reduces the time needed for CT processing and PSI design without increasing the time for size planning, accurately predicts the component size, and improves the accuracy of lower limb alignment in TKA patients, providing a meaningful supplement to the application of AI in orthopaedics.

Introduction This study aims to evaluate the efficacy of a computer vision system in guiding exercise management for patients with knee osteoarthritis (OA) by comparing functional improvement between a tele-rehabilitation versus an outpatient intervention program. Methods and analysis This is a prospective, single-blind, randomised controlled trial of 60 patients with knee OA who will be randomly assigned to exercise therapy (n=30) or control (n=30) . Both groups will receive treatment two times per week for 12 weeks. The primary outcome of the study will be assessed using the University of Western Ontario and McMaster University Osteoarthritis Index (WOMAC). The Knee Injury and Osteoarthritis Outcome Score will be assessed, as well as the visual analogue scale, quality of life score and physical fitness score. All observations will be collected at baseline and at weeks 4, 8 and 12 during the intervention period, as well as at weeks 4, 8, 12 and 24 during the follow-up visits after the end of the intervention. Ethics and dissemination This evaluator-blinded, prospective, randomised controlled study was approved by the Biomedical Ethics Review Committee of West China Hospital of Sichuan University. Trial registration number ChiCTR2300070319.

Background: Artificial intelligence and large language models (LLMs) are emerging as transformative technologies in medicine. However, their ability to develop physical exercise rehabilitation programs and provide insights into musculoskeletal (MSK) disorders remains underexplored. This study aimed to evaluate the quality and readability of LLM-generated responses to consultation questions addressing various stages of the clinical process encountered by patients with MSK disorders. Methods: This study recruited 50 patients with musculoskeletal disorders and extracted disease-related frequently asked questions from Google search. We developed three clinical scenario-based question types simulating real consultations, which were processed by four LLMs (GPT-3.5-turbo, GPT-4-turbo, GPT-4o, and Claude-3-haiku-20240307). Response quality was assessed by orthopedic specialists and therapists using the DISCERN instrument, and GPT-4o-assisted evaluation was used to extend the assessment after validation with expert ratings. Readability was systematically assessed via six validated indices. Results: Among the 1476 LLM-generated responses, the generated rehabilitation programs demonstrated consistent adherence to the specified query requirements. The DISCERN scores ranged from 26 (poor) to 68 (excellent), with a mean score of 55.60 ± 8.40. The intraclass correlation coefficient (0.68) indicated moderate interrater agreement, and Cronbach’s α showed good internal consistency. The readability scores across six indices indicated that most responses exceeded the recommended reading levels (p < 0.05). Conclusions: LLMs generated moderate-to-high-quality PE rehabilitation recommendations for patients with MSK disorders across simulated consultation scenarios. However, limited supporting materials and suboptimal readability may restrict their effectiveness. With physician oversight, improved readability, and enhanced supplementary resources, LLMs demonstrate considerable potential as supportive tools in orthopedics.

Background: The number of patients with primary Sjögren’s syndrome (pSS) who require total knee arthroplasty (TKA) is expected to increase, and there are few studies describing their outcomes. This research was focused on the evaluation of a TKA cohort in pSS patients and to compare outcomes with those of matched individuals from the general population. Methods: From 2004 to 2020, we found 36 TKAs in 30 patients with pSS from the single-institution arthroplasty registry, and they were matched for age, gender, bilateral or unilateral surgery, American Society of Anesthesiologists (ASA) score, and year of surgery with 72 TKAs in 60 osteoarthritis patients without rheumatic diseases (1:2 ratio). Perioperative outcomes were obtained, and clinical evaluations were performed at the last follow-up. Results: After a mean six-year follow-up, both cohorts had similar knee function and health-related quality of life outcomes. The pSS group had more patients with post-operative anemia and hypoalbuminemia and more patients needing platelet transfusion. There were no significant differences in other complications, the rates of 90-day readmission, or overall revision. By multivariate analysis, the influencing factor for anemia in pSS patients was lower preoperative hemoglobin (OR = 0.334, 95% CI (0.125–0.889), p < 0.05). Conclusions: Our study demonstrated that pSS patients who received TKA could achieve comparable clinical outcomes to the general population. However, more attention should be paid to the perioperative hematological management of pSS patients who underwent TKA.