Zhao Lingyi, Lediju Bell Muyinatu A
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, USA.
BME Front. 2022;2022. doi: 10.34133/2022/9780173. Epub 2022 Feb 15.
The massive and continuous spread of COVID-19 has motivated researchers around the world to intensely explore, understand, and develop new techniques for diagnosis and treatment. Although lung ultrasound imaging is a less established approach when compared to other medical imaging modalities such as X-ray and CT, multiple studies have demonstrated its promise to diagnose COVID-19 patients. At the same time, many deep learning models have been built to improve the diagnostic efficiency of medical imaging. The integration of these initially parallel efforts has led multiple researchers to report deep learning applications in medical imaging of COVID-19 patients, most of which demonstrate the outstanding potential of deep learning to aid in the diagnosis of COVID-19. This invited review is focused on deep learning applications in lung ultrasound imaging of COVID-19 and provides a comprehensive overview of ultrasound systems utilized for data acquisition, associated datasets, deep learning models, and comparative performance.
新冠病毒病(COVID-19)的大规模持续传播促使世界各地的研究人员积极探索、了解并开发新的诊断和治疗技术。尽管与X射线和CT等其他医学成像方式相比,肺部超声成像仍是一种不太成熟的方法,但多项研究已证明其在诊断COVID-19患者方面的前景。与此同时,人们构建了许多深度学习模型来提高医学成像的诊断效率。这些最初并行开展的工作相结合,使得多位研究人员报告了深度学习在COVID-19患者医学成像中的应用,其中大多数都展示了深度学习在辅助诊断COVID-19方面的巨大潜力。这篇特邀综述聚焦于深度学习在COVID-19肺部超声成像中的应用,并全面概述了用于数据采集的超声系统、相关数据集、深度学习模型以及比较性能。