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Completed

NCT Number: NCT07737223

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

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.

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Key information

Conditions

Femoroacetabular Impingement Syndrome Femoracetabular Impingement Joint Diseases Musculoskeletal Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms

Age range

12 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

The Fourth Medical Center of Chinese PLA General Hospital

Beijing, Beijing Municipality, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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)

Treatment and study plan

Primary outcomes

  1. Area Under the Receiver Operating Characteristic Curve (AUC) of the AI screening model for identifying FAIS

    Time frame: Through study completion, up to 7 years

    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.

  2. Area Under the Receiver Operating Characteristic Curve (AUC) of the AI diagnostic model for identifying FAIS

    Time frame: Through study completion, up to 7 years

    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.

Secondary outcomes

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

    Time frame: Through study completion, up to 7 years

    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.

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

    Time frame: Through study completion, up to 7 years

    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.

  3. Comparison of AUC between the AI models and clinicians of varying seniority

    Time frame: Through study completion, up to 7 years

    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.

  4. Intraclass Correlation Coefficient (ICC) of automated hip radiographic measurements

    Time frame: Through study completion, up to 7 years

    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.

Sponsors and collaborators

Lead sponsor

ChunBao Li

Other

Collaborators

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

Registry information

Official study title

Development and Validation of AI-Based Screening and Diagnostic Models for Femoroacetabular Impingement Syndrome: A Multicenter Study Integrating Clinical Features and Pelvic Radiographs

Acronym: FAIS-AI

Important dates

Study start
2019
Primary completion
2025
Study completion
2025
First posted
Jul 30, 2026
Registry last updated
Jul 30, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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