Femoroacetabular impingement syndrome (FAIS) is the leading cause of hip pain in young adults and frequently progresses to osteoarthritis, often exacerbated by delayed diagnosis in primary care. Current AI models for FAIS diagnosis primarily rely on single imaging modalities, limiting their diagnostic accuracy and clinical utility.
This multicenter, retrospective-prospective study aims to develop and validate AI-based screening and diagnostic models for FAIS by integrating multimodal clinical features and pelvic radiographic data. A retrospective cohort of 1,841 patients (January 2019 to January 2025) was collected from four tertiary centers in Beijing (First and Fourth Medical Centers of PLA General Hospital, Beijing Friendship Hospital, and Rocket Force Characteristic Medical Center) for model development and internal validation. A screening model was built using the 10 most contributory clinical features (identified via SHAP analysis from 47 consensus-based features) with a fully connected neural network. A diagnostic model was built by combining clinical features, automated hip radiographic measurements (CE Angle, Tonnis Angle, Alpha Angle, Femoral Neck-Shaft Angle via CenterNet), and hip X-ray images (via YOLOv8 + CNN) through a dual-channel hybrid deep learning architecture. Prospective external validation was performed on an independent cohort of 776 patients from four population groups (large hospital, athletic, student, community) between February and November 2025. Model performance was evaluated using AUC, sensitivity, specificity, accuracy, PPV, NPV, and decision curve analysis, and compared against five physicians of varying seniority. The study aims to address FAIS diagnostic delays by providing an AI-based solution suitable for patient self-assessment, primary care screening, and specialist referral decision-making.