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
Among 11 studies in Femoracetabular Impingement with a site in China — 1 open to participants now, 2 under way, none of them phase 3. Femoracetabular Impingement · China.
Among the 11 studies in Femoracetabular Impingement with a site in China, this one is one of 3 that started in 2019, is one of 9 observational ones, and was the 10th registered.
116 studies on the registry are indexed under Femoracetabular Impingement; 37 are open to participants now.
Browse Femoracetabular Impingement studies →1 of the 11 studies in Femoracetabular Impingement with a site in China is open now. Recruiting first, the newest first:
Peking University Third Hospital · Sites in China
A study can be open while its sites in China have not opened yet or have stopped. Only those recruiting at a site in China →
Of the 19 Femoracetabular Impingement studies recruiting now, 6 (32%) set no upper age limit and 11 (58%) stop at 75 or younger; 3 take children under 18; 6 take healthy volunteers.
This study: 12 Years and older · all sexes · no healthy volunteers.
The full table on the hub →This tab as a report to print or save as PDF → With an account.
11 studies; the count open now beside each. This study's row is marked.
First in 2018; this study’s year is marked. 3 started in 2019.
This study's enrollment of 2,617 is above the median of 88 across 37 observational studies indexed under Femoracetabular Impingement.
Median enrollment 315 across the 2 interventional studies, 132 across the 9 observational. Of the 6 that completed or were terminated, 0 (0%) have results posted.
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None of the 11 has an industry lead sponsor. The 2 most frequent:
ChunBao Li is the lead sponsor of 4 studies on the registry; 2 are open to participants now.
ChunBao Li has 2 studies filed under Femoracetabular Impingement — fewer than ten, so there is no comparison to draw.
None of the studies under way reaches primary completion within a year.
Among the 11 studies in Femoracetabular Impingement with a site in China, from their registry updates:
This tab as a report to print or save as PDF → With an account.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
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
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)
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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
Ran from Jan 2019 to primary completion in Nov 2025, 82 months.
How these figures are worked out, and which studies are left out of them.
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