Selection of joint replacement methods based on AI cartilage model.

To explore the application value of an artificial intelligence (AI)-based cartilage model evaluation system in the decision-making of knee joint replacement procedures (total knee arthroplasty [TKA] vs. unicompartmental knee arthroplasty [UKA]), and to compare the dynamic differences in postoperative functional recovery between the two procedures. Eighty patients with advanced knee osteoarthritis evaluated by the AI cartilage model were retrospectively included, with 40 patients in each of the TKA group and the UKA group. The AI model performed thin-slice MRI scans of the knee joints, selected the fat-suppressed three-dimensional isotropic intermediate-weight VISTA sequence to improve image identification, and imported the data into an MRI-based intelligent reconstruction system to achieve intelligent segmentation and reconstruction of MRI. It reconstructed 15 structures including the femur, tibia, patella, their cartilages, and ligaments. The surgical procedures were decided based on the cartilage damage. The Knee Society Score (KSS, including knee score and function score), Visual Analog Pain Score (VAS), and WOMAC Osteoarthritis Index (pain, stiffness, function dimensions) were recorded before surgery and 1, 3, 6, and 12 months after surgery in both groups. Simultaneously record the preoperative and postoperative hip–knee–ankle (HKA) angles and their differences for both patient groups, the location of preoperative pain, and the preoperative and 12-month postoperative knee range of motion (ROM) and their differences. There were no statistically significant differences in gender and average BMI between the unicompartmental knee replacement group and the total knee replacement group (P > 0.05); however, there was a statistically significant difference in average age between the two groups (P < 0.05). Within each group, there were statistically significant differences in all scores (KSS knee score, KSS function score, VAS score, WOMAC score) at 1 month, 3 months, 6 months, and 12 months after surgery compared with those before surgery (P < 0.05). In the inter-group comparison (comparison between unicompartmental knee replacement patients and total knee replacement patients), for the KSS knee score, P = 0.27 before surgery, P < 0.05 at 1 month after surgery; P = 0.46 at 3 months after surgery, P = 0.33 at 6 months after surgery, and P = 0.08 at 12 months after surgery. For the KSS function score, P < 0.05 before surgery, P < 0.05 at 1 month after surgery, P < 0.05 at 3 months after surgery, P = 0.73 at 6 months after surgery, and P = 0.76 at 12 months after surgery. For the VAS score, P = 0.51 before surgery, P < 0.05 at 1 month and 3 months after surgery, P = 0.82 at 6 months after surgery, and P = 0.80 at 12 months after surgery. For the WOMAC score, P < 0.05 at all time points before and after surgery. In the unicompartmental knee arthroplasty (UKA) group, the preoperative hip–knee–ankle (HKA) angle was 175.8° ± 1.4°, the postoperative HKA angle was 178.6° ± 1.0°, and the change was 2.9° ± 0.6°. In the total knee arthroplasty (TKA) group, the preoperative HKA angle was 175.1° ± 2.5°, the postoperative HKA angle was 179.2° ± 0.9°, and the change was 8.1° ± 1.8°. Comparison between the two groups showed no significant difference in the preoperative HKA angle (p = 0.12), whereas the postoperative HKA angle and the change from baseline were significantly different (both p < 0.05). In the UKA group, pain was located medially in 35 cases (87.5%) and laterally in 5 cases (12.5%). In the TKA group, pain was located medially in 25 cases (62.5%), laterally in 10 cases (25.0%), and in the patellofemoral joint in 5 cases (12.5%). For range of motion (ROM), the UKA group had a preoperative ROM of 105.8° ± 6.4°, a 12-month postoperative ROM of 130.8° ± 5.2°, and a change of 25.0° ± 1.5°. The TKA group had a preoperative ROM of 89.0° ± 6.8°, a 12-month postoperative ROM of 121.0° ± 4.5°, and a change of 32.0° ± 2.5°. Intergroup comparisons of preoperative, postoperative, and change values all showed statistically significant differences (all p < 0.05). The AI cartilage model can accurately distinguish the indications for TKA and UKA by quantifying the morphology of cartilage damage. UKA is more suitable for local cartilage damage in the medial compartment, and it has a significant advantage in early functional recovery; while TKA has better long-term stability in patients with extensive cartilage degeneration. This study provides an evidence-based basis for AI-assisted personalized selection of knee joint replacement procedures.