Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network

We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanners. METHODS: We enrolled 402 patients who underwent noncontrast CT examinations, including L1–L4 vertebrae, and dual-energy x-ray absorptiometry (DXA) examination. Among these, 280 patients (3360 sagittal vertebral images), 70 patients (280 sagittal vertebral images), and 52 patients (208 sagittal vertebral images) were assigned to the training data set for deep learning model development, the validation, and the test data set, respectively. Bone mineral density and the trabecular bone score (TBS), an index of bone microarchitecture, were assessed by DXA. BMD(DL) and TBS(DL) were predicted by deep learning with a convolutional neural network (ResNet50). Pearson correlation tests assessed the correlation between BMD(DL) and BMD, and TBS(DL) and TBS. The diagnostic performance of BMD(DL) for osteopenia/osteoporosis and that of TBS(DL) for bone microarchitecture impairment were evaluated using receiver operating characteristic curve analysis. RESULTS: BMD(DL) and BMD correlated strongly (r = 0.81, P < 0.01), whereas TBS(DL) and TBS correlated moderately (r = 0.54, P < 0.01). The sensitivity and specificity of BMD(DL) for identifying osteopenia or osteoporosis were 93% and 90%, and 100% and 94%, respectively. The sensitivity and specificity of TBS(DL) for identifying patients with bone microarchitecture impairment were 73% for all values. CONCLUSIONS: The BMD(DL) and TBS(DL) derived from conventional CT images could identify patients who should undergo DXA, which could be a gatekeeper tool for detecting latent osteoporosis/osteopenia or bone microarchitecture impairment.