Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans

Abstract

Purpose To predict whether a patient has osteoporosis/osteopenia using the attenuation of trabecular bone obtained from knee computed tomography (CT) scans. Methods Retrospective analysis of 273 patients who underwent contemporaneous knee CT scans and dual-energy X-ray absorptiometry (DXA) within 1 year. Volumetric segmentation of the trabecular bone of the distal femur, proximal tibia, patella, and proximal fibula was performed to obtain the bone CT attenuation. The data was randomly split into training/validation (78%) and test (22%) datasets and the performance in the test dataset were evaluated. The predictive properties of the CT attenuation of each bone to predict osteoporosis/osteopenia were assessed. Multivariable support vector machines (SVM) and random forest classifiers (RF) were used to predict osteoporosis/osteopenia. Results Patients with a mean age (range) of 67.9 (50–87) years, 85% female were evaluated. Seventy-seven (28.2%) of patients had normal bone mineral density (BMD), 140 (51.3%) had osteopenia, and 56 (20.5%) had osteoporosis. The proximal tibia had the best predictive ability of all bones and a CT attenuation threshold of 96.0 Hounsfield Units (HU) had a sensitivity of .791, specificity of .706, and area under the curve (AUC) of .748. The AUC for the SVM with cubic kernel classifier (AUC = .912) was better than the RF classifier (AUC = .683, P < .001) and better than using the CT attenuation threshold of 96.0 HU at the proximal tibia (AUC = .748, P = .025). Conclusions Opportunistic screening for osteoporosis/osteopenia can be performed using knee CT scans. Multivariable machine learning models are more predictive than the CT attenuation of a single bone.

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The Rising Utilization of Opportunistic CT Screening and Machine Learning in Bone Mineral Density

“…In the paper “Machine Learning for Opportunistic Screening for Osteoporosis and Osteopenia Using Knee CT Scans,” Sebro and Elmahdy demonstrate both the ability to utilize knee CT for opportunistic screening of bone mineral density and how ML models have the potential to outperform conventional mean HU value measurements for identifying patients with low bone mineral density. 4 The work of Sebro and Elmahdy underscores the importance of selecting the appropriate ML model for the desired task. The ability of ML models to predict osteopenia or osteoporosis in the test data set of their study ranged from excellent (AUC = .912) to poor (AUC = .683), which illustrates how selection of a poor ML model can adversely affect outcomes.…”