AI-Based Screening and Diagnostic Models for Femoroacetabular Impingement Syndrome (FAIS-AI)

NCT07737223 | Completed

• 1 other identifier: 2025YL002-KS001
• Study Type: observational
• Target: 2,617
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Femoroacetabular Impingement Syndrome

Keywords

Femoroacetabular Impingement; FAIS; Artificial Intelligence; Deep Learning; Hip Pain; Screening Model; Diagnostic Model; Pelvic Radiograph; Neural Network; YOLOv8; Convolutional Neural Network; CenterNet; SHAP; Hip X-ray

Outcome Measures

Primary Outcomes (2)

• 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.

  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.

  Through study completion, up to 7 years

Secondary Outcomes (4)

• Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) of the AI screening and diagnostic models

  Through study completion, up to 7 years

• Net benefit of the AI models in Decision Curve Analysis (DCA)

  Through study completion, up to 7 years

• Comparison of AUC between the AI models and clinicians of varying seniority

  Through study completion, up to 7 years

• Intraclass Correlation Coefficient (ICC) of automated hip radiographic measurements

  Through study completion, up to 7 years

Study Arms (2)

FAIS Group

Patients diagnosed with Femoroacetabular Impingement Syndrome based on clinical and radiographic criteria.

Non-FAIS Control Group

Patients presenting with hip pain who do not meet diagnostic criteria for FAIS.

Eligibility Criteria

• Age: 12 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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.

You may qualify if:

• 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

You may not qualify if:

• 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)

Sponsors & Collaborators

• The First Medical Center of Chinese PLA General Hospital: collaborator
• ChunBao Li: lead
• Beijing Friendship Hospital: collaborator
• The PLA Rocket Force Characteristic Medical Center: collaborator
• Beijing Sport University Hospital: collaborator
• Beijing Normal University Hospital: collaborator
• Deshengmenwai Community Health Service Center: collaborator
• Beijing Longwood Valley MedTech Co., Ltd.: collaborator

Study Sites (1)

The Fourth Medical Center of Chinese PLA General Hospital

Beijing, Beijing Municipality, China

Location

MeSH Terms

Conditions

Femoracetabular Impingement

Condition Hierarchy (Ancestors)

Joint Diseases; Musculoskeletal Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Deputy Director of Sports Medicine

Study Record Dates

• First Submitted: July 27, 2026
• First Posted: July 30, 2026
• Study Start: January 1, 2019
• Primary Completion: November 30, 2025
• Study Completion: December 30, 2025
• Last Updated: July 30, 2026

Record last verified: 2026-07

Locations

CN (1)