Deep learning based sarcopenia prediction from shear-wave ultrasonographic elastography and gray scale ultrasonography of rectus femoris muscle

Abstract

We aim to evaluate the performance of a deep convolutional neural network (DCNN) in predicting the presence or absence of sarcopenia using shear-wave elastography (SWE) and gray-scale ultrasonography (GSU) of rectus femoris muscle as an imaging biomarker. This retrospective study included 160 pair sets of GSU and SWE images (n = 160) from December 2018 and July 2019. Two radiologists scored the echogenicity of muscle on GSU (4-point score). Among them, 141 patients underwent CT and their L3 skeletal muscle index (SMI) were measured to categorize the presence or absence of sarcopenia. For DCNN, we used three CNN architectures (VGG19, ResNet-50, DenseNet 121). The accuracies of DCNNs for sarcopenia classification were 70.0-80.0% (based on SWE) and 65.0-75.0% (based on GSU). The DCNN application to SWE images highlights the utility of deep-learning base SWE for sarcopenia prediction. DCNN application to SWE images might be a potentially useful biomarker to predict sarcopenic status.