Deep Learning-Based BMD Estimation from Radiographs with Conformal Uncertainty Quantification

TL;DR

This study introduces a deep learning framework for estimating bone mineral density from knee X-rays, incorporating conformal uncertainty quantification to provide reliable patient-specific prediction intervals, advancing osteoporosis screening methods.

Contribution

It presents a novel approach combining deep learning with conformal prediction to quantify uncertainty in BMD estimation from radiographs, a first in this context.

Findings

Pearson correlation of 0.68 for BMD prediction.

Multi-sample TTA yields tighter confidence intervals.

Uncertainty estimates reflect case difficulty.

Abstract

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.