Development and Validation of a Multi-Modal Ensemble Model for Predicting Progression in Idiopathic Scoliosis

Study DesignRetrospective Cohort Study.ObjectivesAccurate prediction of curve progression in idiopathic scoliosis at the initial visit would facilitate clinical decision-making. In our previous study, progression was predicted using deep learning based on frontal whole-spine radiographs. This study aimed to improve prediction accuracy using a multimodal ensemble model integrating frontal and lateral radiographs with clinical information.MethodsThis multicenter retrospective cohort study included 527 patients with idiopathic scoliosis. Based on the change in Cobb angle over two years, patients were classified into progression (≥10°), non-progression (≤5°), and borderline (6-9°) groups. A total of 471 patients (274 progression and 197 non-progression) were analyzed. Input data included initial whole-spine frontal and lateral radiographs and clinical variables (age, sex, Risser sign, and baseline Cobb angle). Deep learning models (Vision Transformer, Swin Transformer, and ConvNeXtV2) and machine learning models (logistic regression, support vector machine [SVM], and random forest) were applied, generating nine models. Predicted probabilities were integrated using weighted averaging. Performance was evaluated using repeated stratified 10-fold cross-validation with the area under the receiver operating characteristic curve (AUC).ResultsMean age was 12.7 ± 1.8 years, and 433 patients (91.9%) were female. Mean initial Cobb angle was 27.8 ± 10.1°. The AUCs based on averaged predicted probabilities within each modality were 0.791 for frontal radiographs, 0.767 for lateral radiographs, and 0.721 for clinical features. The weighted ensemble achieved highest performance (AUC 0.819, 95% CI: 0.816-0.821), significantly outperforming other ensemble methods (P < 0.001).ConclusionsA multimodal ensemble model integrating radiographs and clinical data improved prediction of idiopathic scoliosis progression compared with single-modality models.