Preprint · Apr 23, 2026
Deep Learning-Based Detection of Osteoporosis and Osteopenia Using Wrist X-rays: A Retrospective Analysis with Internal Validation (Preprint)
By Tsai YY, Huang TP, Tseng HS et al.
BACKGROUND Osteoporosis is a common degenerative disorder in older adults that can lead to fractures, necessitating early diagnosis. OBJECTIVE This study trained and validated deep learning (DL) models to detect osteoporosis and osteopenia through wrist X-rays, aiming to offer a more convenient and time-saving alternative to the established measures for bone mineral density (BMD) assessment. METHODS This retrospective study gathered data from patients aged 20 years and older who underwent wrist X-rays and BMD testing via dual-energy X-ray absorptiometry (DEXA) at Tri-Service General Hospital in Taiwan. The Residual Network (ResNet) model were used to train and validate a deep learning model on wrist X-rays, differentiating osteoporosis and osteopenia from normal bone conditions. RESULTS A total of 3,825 wrist images with corresponding BMD measurements were utilized to train a DL model. The model demonstrated excellent performance in discriminating normal BMD from osteopenia/osteoporosis, achieving an area under the ROC curve (AUC) of 0.9696, with a sensitivity of 99.5% and a specificity of 86.1%. Further, in the subset of training data which excluded wrist images of osteopenia, the model excelled in differentiating osteoporosis from normal BMD, yielding an AUC of 0.9741, with 90.2% sensitivity and 96.5% specificity. CONCLUSIONS Our DL model excels at distinguishing normal BMD from osteoporosis using wrist X-rays. It provides a time-efficient and convenient alternative to traditional BMD measurements via DEXA, making it particularly beneficial in resource-limited environments. By integrating this model into standard wrist X-ray exams, we enable more accessible assessments, which support early detection and the development of effective treatment strategies, lowering resource expenditures.