Can an Artificial Intelligence Application Raise Awareness of Dislocation Risk After Total Hip Arthroplasty?

Background: Dislocation after total hip arthroplasty (THA) is a devastating complication. The hip-spine relationship is a significant contributor to hip instability and dislocation after THA but is predominantly evaluated with static radiographs, limiting its utility. This study evaluated a novel artificial intelligence (AI)-based application for real-time analysis of hip-spine motion prior to THA to dynamically evaluate patients' hip-spine stiffness in real-time prior to THA. Methods: Preoperative hip and spine flexibility were assessed using an AI application that recorded patients performing sit-to-stand, forward flexion, and standing posture maneuvers. Minimum and maximum neck, spine, trunk, and knee angles were measured preoperatively. Preoperative radiographs were also evaluated for spinal stiffness indicators. Acetabular component abduction and anteversion angles were measured to confirm adequate positioning. Results: Nineteen patients underwent THA via an anterior-based muscle-sparing approach with a minimum 12-month follow-up. The mean preoperative forward flexion trunk angle was 95.7° ± 14.4° (25th percentile: ≤87.2°). During sit-to-stand, mean maximum and minimum spine angles were 38.3° ± 13.3° (25th percentile: ≤27.6°) and 5.1° ± 5.9° (75th percentile: ≥6.2°), respectively. Fifteen patients (78.9%) received 36-mm femoral heads. Mean abduction and anteversion was 43.9° and 26.4°, respectively. No postoperative hip dislocations occurred. Conclusions: This AI-based hip joint assessment tool may serve as a clinic-based tool to evaluate the hip-spine relationship as a dynamic predictor of dislocation risk. It may offer greater accuracy than static radiographs, which cannot comprehensively capture real-time functional movements. This tool may improve surgical planning, particularly in higher-risk patients. Larger studies are needed to validate its predictivity and clinical utility.