Deep Learning of Lateral Thoracolumbar Radiographs and Clinical Risk Factors for Incident Vertebral Fracture:...

Early identification of patients at risk of incident vertebral fracture remains challenging because routine clinical risk assessment does not fully capture local spinal fragility. This single-center retrospective cohort study evaluated whether deep learning (DL) features extracted from baseline thoracolumbar lateral radiographs improve the prediction of incident vertebral fracture within 2 years when combined with clinical risk factors. A total of 2,173 patients were included and chronologically divided into a derivation cohort (n = 1,449) and an internal validation cohort (n = 724). DL features were derived from baseline radiographs, and LASSO-Cox regression was used to select predictors and build a clinical model, a DL model, and a combined model. Performance was assessed by bootstrap optimism correction, temporal internal validation, calibration, decision curve analysis, time-dependent net reclassification improvement (NRI), integrated discrimination improvement (IDI), and sensitivity analyses. Of 2,048 candidate DL features, 5 were retained to generate a DL score, which remained an independent predictor in the combined model (HR 1.64, 95% CI 1.34-2.01; P < 0.001). In internal validation, the combined model achieved a C-index of 0.759, a 2-year AUC of 0.774, and a 2-year Brier score of 0.077, all superior to the clinical model, with good calibration (intercept 0.012; slope 0.972). Compared with the clinical model, the combined model also improved reclassification (2-year NRI 0.316 in derivation and 0.241 in validation) and discrimination (2-year IDI 0.047 and 0.033, respectively; all P < 0.01), and provided greater net benefit on decision curve analysis. Sensitivity analyses were consistent with the primary results. Combining DL features from thoracolumbar lateral radiographs with clinical risk factors may enable more accurate individualized prediction of incident vertebral fracture within 2 years.