Development and Validation of AI-Based Screening and Diagnostic Models for Femoroacetabular Impingement Syndrome: A Multicenter Study Integrating Clinical Features and Pelvic Radiographs
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 2,617
- 试验地点
- 1
- 主要终点
- Area Under the Receiver Operating Characteristic Curve (AUC) of the AI screening model for identifying FAIS
研究概览
简要总结
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 12 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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)
结局指标
主要结局
Area Under the Receiver Operating Characteristic Curve (AUC) of the AI screening model for identifying FAIS
时间窗: 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
时间窗: 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.
次要结局
- 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)
研究者
ChunBao Li
Deputy Director of Sports Medicine
Chinese PLA General Hospital