Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea.
Department of Radiology, Research Institute of Radiological Science, and Center for Clinical Imaging Data Science (CCIDS), Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, South Korea.
Sci Rep. 2022 Mar 4;12(1):3596. doi: 10.1038/s41598-022-07683-6.
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.
我们旨在评估深度卷积神经网络（DCNN）在使用剪切波弹性成像（SWE）和股直肌灰阶超声（GSU）作为成像生物标志物预测肌少症方面的性能。这项回顾性研究包括了 2018 年 12 月至 2019 年 7 月的 160 对 GSU 和 SWE 图像（n=160）。两位放射科医生对 GSU 上的肌肉回声进行了评分（4 分制）。其中，141 名患者接受了 CT 检查，测量了他们的 L3 骨骼肌指数（SMI）以分类是否存在肌少症。对于 DCNN，我们使用了三种 CNN 架构（VGG19、ResNet-50、DenseNet 121）。DCNN 对肌少症分类的准确率为 70.0-80.0%（基于 SWE）和 65.0-75.0%（基于 GSU）。DCNN 对 SWE 图像的应用突出了深度学习基础 SWE 在肌少症预测中的实用性。DCNN 对 SWE 图像的应用可能是预测肌少症状态的一种有潜在价值的生物标志物。
