Mohammadi, Farid Ghareh

Patients with low bone mineral density (BMD) face an increased risk of fractures, yet are frequently undiagnosed. Consequently, it is imperative to have opportunistically screen for low BMD in patients undergoing other medical evaluations. This retrospective study encompassed 422 patients aged ≥ 50 who underwent both dual-energy X-ray absorptiometry (DXA) and hand radiographs (modality of digital X-ray) from three different vendors within a 12-month period. The dataset was randomly divided into training/validation (n=338) and test (n=84) datasets. we sought to predict osteoporosis/osteopenia and establish correlations between bone textural analysis and DXA measurements. Our results demonstrate that the deep learning model achieved an accuracy of 77.38%, sensitivity of 77.38%, specificity of 73.63%, and an area under the curve (AUC) of 83% in detecting osteoporosis/osteopenia. These findings suggest that hand radiographs can serve as a viable screening tool for identifying individuals warranting formal DXA assessment for osteoporosis/osteopenia.

Patients with low bone mineral density (BMD) are at risk for fractures however are often undiagnosed. Therefore, there is a need to opportunistically screen for low BMD in patients who present for other studies. This is a retrospective study of 812 patients aged 50 years or older who had dual-energy X-ray absorptiometry (DXA) and radiographs of the hands within 12 months of each other. This dataset was randomly split into training/validation (n=533) and test (n=136) datasets. A deep learning (DL) framework was used to predict osteoporosis/osteopenia. Correlations between the textural analysis of the bones and DXA measurements were obtained. We found that the DL model had an accuracy of 82.00%, sensitivity of 87.03%, specificity of 61.00% and an area under the curve (AUC) of 74.00% to detect osteoporosis/osteopenia. Our findings show that radiographs of the hand can be used to screen for osteoporosis/osteopenia and identify patients who should get formal DXA evaluation.