Femoroacetabular Impingement SyndromeFAIS-AINCT07737223
AI-Based Screening and Diagnostic Models for Femoroacetabular Impingement Syndrome (FAIS-AI)
An observational study of Femoroacetabular Impingement Syndrome, run by ChunBao Li at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 12 years and older
- Conditions the study is for
- Femoroacetabular Impingement Syndrome
- How many people
- 2,617 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 30 Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 12 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- 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)
Trial locations and contacts
1 site in 1 country.
China1 site
The Fourth Medical Center of Chinese PLA General Hospital
Beijing, Beijing Municipality
About this study
Official title: Development and Validation of AI-Based Screening and Diagnostic Models for Femoroacetabular Impingement Syndrome: A Multicenter Study Integrating Clinical Features and Pelvic Radiographs
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
- Medical subject headings
- Femoracetabular Impingement
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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.
Measured: 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
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.
Measured: 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.
Measured: 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.
Measured: 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.
Measured: Through study completion, up to 7 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jul 2026
- Study start1 Jan 2019
- Primary completion30 Nov 2025
- Study completion30 Dec 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Femoroacetabular Impingement SyndromeStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 2,617 participants (actual)
Sponsor and investigators
- Lead sponsor
- ChunBao Li
- Sponsor type
- University, hospital or other
- Collaborators
- Beijing Friendship Hospital, PLA Rocket Force Characteristic Medical Center, Beijing Sport University Hospital, Beijing Normal University Hospital, Deshengmenwai Community Health Service Center, Beijing Longwood Valley MedTech Co., Ltd., The First Medical Center of Chinese PLA General Hospital
- Responsible party
- Sponsor-investigator, ChunBao Li, Chinese PLA General Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07737223
- Sponsor's protocol ID
- 2025YL002-KS001
- Acronym
- FAIS-AI
- Status last verified
- Jul 2026
- First submitted
- 27 Jul 2026
- Last updated
- 30 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of AI-Based Screening and Diagnostic Models for Femoroacetabular Impingement Syndrome: A Multicenter Study Integrating Clinical Features and Pelvic Radiographs
- ClinicalTrials.gov ID
- NCT07737223
- Acronym / sponsor ID
- FAIS-AI / 2025YL002-KS001
- Conditions
- Femoroacetabular Impingement Syndrome
- Keywords
- Femoroacetabular Impingement, FAIS, Artificial Intelligence, Deep Learning, Hip Pain, Screening Model, Diagnostic Model, Pelvic Radiograph
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 2,617 participants (actual)
- Lead sponsor
- ChunBao LiUniversity, hospital or other
- Collaborators
- Beijing Friendship Hospital, PLA Rocket Force Characteristic Medical Center, Beijing Sport University Hospital, Beijing Normal University Hospital, Deshengmenwai Community Health Service Center, Beijing Longwood Valley MedTech Co., Ltd., The First Medical Center of Chinese PLA General Hospital
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 30 Nov 2025 (actual)
- Study completion
- 30 Dec 2025 (actual)
- First posted
- 30 Jul 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 12 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (4 listed)
- 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)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
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.
4 secondary outcomes
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
Net benefit of the AI models in Decision Curve Analysis (DCA)
Time frame: Through study completion, up to 7 years
Comparison of AUC between the AI models and clinicians of varying seniority
Time frame: Through study completion, up to 7 years
Intraclass Correlation Coefficient (ICC) of automated hip radiographic measurements
Time frame: Through study completion, up to 7 years
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Jul 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Femoroacetabular Impingement Syndrome, run by ChunBao Li at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 30 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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 study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Jul 2026.