Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification
This addresses osteoporosis screening accessibility by providing a potential alternative using routine radiographs, though it is incremental as it builds on existing deep learning and conformal prediction methods.
This proof-of-concept study tackled the problem of limited DXA access for osteoporosis screening by proposing a deep learning method to estimate Bone Mineral Density (BMD) from widely available knee X-rays, achieving a Pearson correlation of 0.68 with statistically rigorous uncertainty quantification.
Limited DXA access hinders osteoporosis screening. This proof-of-concept study proposes using widely available knee X-rays for opportunistic Bone Mineral Density (BMD) estimation via deep learning, emphasizing robust uncertainty quantification essential for clinical use. An EfficientNet model was trained on the OAI dataset to predict BMD from bilateral knee radiographs. Two Test-Time Augmentation (TTA) methods were compared: traditional averaging and a multi-sample approach. Crucially, Split Conformal Prediction was implemented to provide statistically rigorous, patient-specific prediction intervals with guaranteed coverage. Results showed a Pearson correlation of 0.68 (traditional TTA). While traditional TTA yielded better point predictions, the multi-sample approach produced slightly tighter confidence intervals (90%, 95%, 99%) while maintaining coverage. The framework appropriately expressed higher uncertainty for challenging cases. Although anatomical mismatch between knee X-rays and standard DXA limits immediate clinical use, this method establishes a foundation for trustworthy AI-assisted BMD screening using routine radiographs, potentially improving early osteoporosis detection.