A Review of Deep Learning Applications in Lung Ultrasound Imaging of COVID-19 Patients

作     者：Lingyi Zhao Muyinatu A.Lediju Bell 

作者机构：Department of Electrical and Computer EngineeringJohns Hopkins UniversityBaltimoreUSA Department of Computer ScienceJohns Hopkins UniversityBaltimoreUSA Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreUSA 

出 版 物：《Biomedical Engineering Frontiers》 (生物医学工程前沿（英文）)

年 卷 期：2022年第3卷第1期

页      面：31-47页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：National Institutes of Health  NIH 

主　　题：diagnosis lung Lung 

摘      要：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 *** 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 *** the same time,many deep learning models have been built to improve the diagnostic efficiency of medical *** 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 *** 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.