An observational study in Femoroacetabular Impingement Syndrome, sponsored by ChunBao Li. Completed at 1 site in China. Open to participants aged 12 Years and older. Per ClinicalTrials.gov, last updated 2026-07-30.
Sponsored by ChunBao Li · Observational
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.
Patients aged 12 years and older who presented with hip pain and underwent hip X-ray examination at the participating institutions between January 2019 and November 2025.
Patients presenting to the outpatient clinic with a chief complaint of hip pain
Meeting preliminary clinical suspicion of hip pathology (based on history and physical examination)
Willing and able to provide written informed consent
Exclusion Criteria:
Groin or thigh hematoma, or abdominal/pelvic masses (identified on physical examination or imaging)
Non-musculoskeletal conditions causing hip-region pain (e.g., urinary tract disorders, gynecological conditions)
Signs of active infection (fever with elevated C-reactive protein)
Incomplete or substandard clinical or imaging data (e.g., poor-quality radiographs, missing key variables)
Patients diagnosed with Femoroacetabular Impingement Syndrome based on clinical and radiographic criteria.
Patients presenting with hip pain who do not meet diagnostic criteria for FAIS.
Area Under the Receiver Operating Characteristic Curve (AUC) of the AI screening model for identifying FAIS
The AI screening model integrates 10 key clinical features identified through SHAP analysis using a fully connected neural network. AUC will be calculated from the receiver operating characteristic (ROC) curve, with values ranging from 0.5 (no discrimination) to 1.0 (perfect discrimination), to evaluate the screening model diagnostic performance.
Time frame: Through study completion, up to 7 years
Area Under the Receiver Operating Characteristic Curve (AUC) of the AI diagnostic model for identifying FAIS
The AI diagnostic model combines 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. AUC will be calculated from the ROC curve to evaluate the comprehensive diagnostic performance.
Time frame: Through study completion, up to 7 years
Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) of the AI screening and diagnostic models
Sensitivity, specificity, PPV, and NPV will be calculated at the optimal threshold determined from the ROC curve analysis (Youden index). These metrics provide clinically meaningful measures of the models diagnostic accuracy for FAIS detection in real-world clinical settings.
Time frame: Through study completion, up to 7 years
Net benefit of the AI models in Decision Curve Analysis (DCA)
Decision curve analysis will be performed to assess the clinical net benefit of the AI screening and diagnostic models across a range of threshold probabilities. This analysis evaluates whether using the AI models for clinical decision-making provides greater benefit than treating all or treating none.
Time frame: Through study completion, up to 7 years
Comparison of AUC between the AI models and clinicians of varying seniority
The AUC of the AI models will be compared against the diagnostic performance of five physicians with varying levels of clinical experience (ranging from junior resident to senior attending physician) using DeLong test. Statistical significance will be set at p less than 0.05.
Time frame: Through study completion, up to 7 years
Intraclass Correlation Coefficient (ICC) of automated hip radiographic measurements
The agreement between automated measurements (CE Angle, Tonnis Angle, Alpha Angle, Femoral Neck-Shaft Angle obtained via CenterNet) and manual measurements performed by two independent radiologists will be evaluated using the Intraclass Correlation Coefficient (ICC). ICC values greater than 0.75 indicate good reliability and greater than 0.90 indicate excellent reliability.
Time frame: Through study completion, up to 7 years
This study is completed, as verified in Jul 2026. You cannot join it, but the record below documents what was studied.
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ChunBao Li