Prediction of bone mineral density from computed tomography: application of deep learning with a convolutional neural network

Objectives To investigate whether a deep learning model can predict the bone mineral density (BMD) of lumbar vertebrae from unenhanced abdominal computed tomography (CT) images. Methods In this Institutional Review Board–approved retrospective study, patients who received both unenhanced CT examinations and dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae, in two institutions (1 and 2), were included. Supervised deep learning was employed to obtain a convolutional neural network (CNN) model using axial CT images, including the lumbar vertebrae as input data and BMD values obtained with DXA as reference data. For this purpose, 1665 CT images from 183 patients in institution 1, which were augmented to 99,900 (= 1665 × 60) images (noise adding, parallel shift and rotation were performed), were used. Internal (by using data of 45 other patients in institution 1) and external validations (by using data of 50 patients in institution 2) were performed to evaluate the performance of the trained CNN model. Correlations and diagnostic performances were evaluated with Pearson’s correlation coefficient ( r ) and area under the receiver operating characteristic curve (AUC), respectively. Results The estimated BMD values, according to the CNN model (BMD CNN ), were significantly correlated with the BMD values obtained with DXA ( r = 0.852 ( p