Predicting skeletal age from HR-pQCT imaging

High-resolution peripheral quantitative computed tomography (HR-pQCT) provides detailed images of bone structure, but the volume and complexity of data can make interpretation difficult. In this study, we developed a deep learning model to estimate skeletal age from HR-pQCT scans, offering a simplified and interpretable measure of bone health. By translating complex imaging data into an age-based summary, this approach may enhance clinical use of HR-pQCT, support early identification of individuals at risk of accelerated bone loss, and improve patient understanding by providing a relatable measure of skeletal integrity.