Deep Learning–based Bone Mineral Density Prediction Using Pediatric Chest Radiographs: A Multicenter Feasibility Study

Background Measuring bone mineral density (BMD) is essential for pediatric bone health assessment. Dual-energy x-ray absorptiometry (DXA) is the reference standard but has limited accessibility. Purpose To develop and evaluate an artificial intelligence model for predicting BMD from pediatric chest radiography. Materials and Methods This retrospective study included patients aged younger than 18 years who underwent DXA and chest radiography within 3 months at two tertiary hospitals (internal test, 2014-2023; external test, 2022-2023). The internal dataset was temporally split into development (fivefold cross-validation) and test sets. The model combined chest radiographs and clinical variables (age, sex, height, and weight) to predict the lumbar spine (L1 through L4) areal BMD, with Z scores calculated from Korean pediatric reference. Performance was evaluated using the Pearson correlation coefficient (r) for regression and the area under the receiver operating characteristic curve (AUC) for detecting low BMD (Z score ≤ -2.0). Results A total of 1464 radiograph-DXA pairs (median age, 13 years IQR, 11-16 years; 824 boys) were included: 774 in the development set, 376 in the internal test set, and 314 in the external test set. The predicted BMD Z scores were strongly correlated with the DXA scores in both the internal (r = 0.85 95% CI: 0.82, 0.88; P r = 0.76 95% CI: 0.71, 0.81; P Z scores and identified low BMD. © RSNA, 2026 Supplemental material is available for this article.