Enhanced diagnosis of osteoporosis using vision transformer with lumbar MRI

Osteoporosis (OP), characterized by bone mineral density (BMD) loss and microstructural deterioration, remains underdiagnosed due to the limitations of conventional methods (DXA/QCT). Early and accurate diagnosis of OP is crucial for optimizing treatment strategies and improving prognosis. To develop and validate a predictive model integrating clinical data, MRI radiomics, and Vision Transformer (ViT) features for enhanced diagnosis and risk assessment of OP. This retrospective dual-center study enrolled 1,095 patients with chronic low back pain (median age: 69 years; 60% female). We developed a 3D ViT model using combined T1WI and T2WI lumbar MRI, simultaneously extracting ViT-based deep features and radiomic features from segmented L1-L3 vertebrae. Feature selection was performed using t-test and LASSO regression. Logistic regression classifiers were constructed to compare standalone ViT and radiomics models, followed by an integrated model incorporating clinical variables, radiomic features, and ViT features. Model performance was assessed using AUC, accuracy, sensitivity, specificity, F1 score, precision, confusion matrices, calibration curves, and decision curve analysis (DCA). Interpretability was achieved through clinical nomogram and SHAP visualization. Among 1,095 patients (age 69[9] years; 657 [60%] female), age and gender emerged as clinical risk factors. The MRI-based ViT model achieved higher AUCs than the radiomics model in both internal (0.844 vs. 0.697) and external (0.745 vs. 0.654) test sets. The combined model demonstrated superior performance with AUCs of 0.855 (internal) and 0.806 (external). The combined model significantly improves OP diagnostic accuracy and clinical utility, with ViT features critically enhancing predictive performance, establishing a promising tool for OP diagnosis and management.