An exploratory study of explainable deep learning for predicting bone mineral density using clavicle features on chest radiographs: A multi-task approach with regression and segmentation.

purposeAlthough bone mineral density (BMD) measurement using dual-energy x-ray absorptiometry (DXA) is the most common method of diagnosing osteoporosis, it is not widely used to screen patients. In this exploratory study, we developed a multi-task learning model that predicts BMD from chest radiographs using clavicular features and supports network explainability.

methodsThe proposed multi-task learning model integrates segmentation and regression tasks by incorporating a regression branch into the U-Net architecture in an end-to-end manner. A total of 1600 patients who underwent chest radiography and DXA of the lumbar vertebrae were included in this study. We compared the BMD predictive performance of the mean absolute error (MAE) and Pearson correlation coefficient (R value) between the proposed multi-task learning model and the single-task learning model, which was defined as the comparison model that only performed BMD prediction. Additionally, model performance for classifying osteoporosis, osteopenia, and normal bone status was evaluated via reclassification analysis based on the World Health Organization (WHO) criteria. Confusion matrices were generated, and classwise and macro-averaged performance metrics were calculated. To confirm the rationale for the BMD predictions, we evaluated heat maps generated using the gradient-weighted class activation mapping technique to determine whether the highlighted regions overlapped with the clavicle.

resultsThe multi-task learning model demonstrated superior predictive performance (MAE of 0.092 g/cm

conclusionThe proposed multi-task learning model demonstrated the predictive rationale by focusing on the clavicle in chest radiographs, which is clinically relevant to BMD, and showed improved performance compared with the single-task model.