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.
Highlights
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
In predicting sarcopenia with GSU images, Grad-CAM was applied and showed high activations in hyperechoic areas due to muscle fascia/fibrosis and hypoechoic areas considered as intramuscular fat area (Fig. 2)
Some researchers found that muscle quality rather than quantity determines muscle function[6,19]
Summary
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. Imaging analysis and interpretation of radiologic imaging are basic tasks performed by radiologists in providing qualitative radiologic reading, depending on their experience and medical knowledge In this era of big data and artificial intelligence, radiologic imaging has been enhanced with a capability to provide quantitative imaging biomarkers for early detection, further characterization, activity monitoring, and response to treatment. To date, there has been no study that predicts the sarcopenia on muscle USG using either DCNNs or radiomics
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