Radiomics and deep learning model based on X-ray imaging for the assisted diagnosis of early Legg-Calvé-Perthes disease

• Published in: BMC musculoskeletal disorders Vol. 26; no. 1; pp. 910 - 14
• Main Authors: Zhang, Dian, Li, Ya-nan, Li, Cheng-long, Guo, Wan-liang
• Format: Journal Article
• Language: English
• Published: London BioMed Central 01.10.2025; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Artificial intelligence; Computer-aided medical diagnosis; Data mining; Deep learning; Diagnosis; Diagnosis, Radioscopic; Disease; Early detection; Epidemiology; Hip; Internal Medicine; Legg-Calve-Perthes disease; Legg-Calvé-Perthes disease; Machine learning; Medical diagnosis; Medicine; Medicine & Public Health; Methods; Orthopedics; Osteochondrosis; Pediatrics; Radiomics; Rehabilitation; Reproducibility; Rheumatology; Sports Medicine; X-ray image; X-rays; China; Deep learning; Legg-Calvé-Perthes disease; X-ray image; Early detection; Radiomics
• ISSN: 1471-2474; 1471-2474
• DOI: 10.1186/s12891-025-09189-4

Background X-rays are the most commonly used method for diagnosing Legg–Calvé–Perthes disease (LCPD) in children. However, in early-stage LCPD, X-rays only show subtle changes, making diagnosis highly dependent on the radiologist’s experience. The aim of this study was to develop and validate a combined radiomics and deep learning model based on anteroposterior and frog-leg lateral X-rays for identifying early LCPD patients. Methods We retrospectively collected imaging data of children diagnosed with early LCPD and normal control groups from two centers between January 2013 and December 2023. Logistic regression (LR), Support vector machine (SVM), and Extreme gradient boosting (XGBoost) algorithms were used to build radiomics and Deep Learning (DL) models, and the performance of individual models was compared. The development of the ensemble model involved a stacking strategy to integrate the best radiomics and DL models. The diagnostic performance of the combined model was compared with that of radiologists of varying experience levels. Model evaluation was conducted using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Finally, the model was validated using calibration and clinical decision curves. Results A total of 200 early LCPD hips (Center A, n  = 157; Center B, n  = 43) and 236 normal hips (Center A, n  = 188; Center B, n  = 48) were included. Among the individual radiomics and DL models, the XGBoost algorithm performed the best: the radiomics model achieved an AUC of 0.845 (95% CI, 0.758–0.933), and the DL model achieved an AUC of 0.848 (95% CI, 0.766–0.929). The ensemble model’s performance was further improved, with an AUC of 0.878 (95% CI, 0.810, 0.945). The combined model significantly outperformed junior radiologists. Calibration and clinical decision curves demonstrated that the combined model had high predictive value. Conclusion The integrated radiomics and DL model using both anteroposterior and frog-leg lateral X-rays demonstrated superior performance over individual radiomics or DL approaches, highlighting its potential as an effective tool for early screening of LCPD.