Medical imaging and computational image analysis in COVID-19 diagnosis: A review

作     者：Nabavi, Shahabedin Ejmalian, Azar Moghaddam, Mohsen Ebrahimi Abin, Ahmad Ali Frangi, Alejandro F. Mohammadi, Mohammad Rad, Hamidreza Saligheh 

作者机构：Shahid Beheshti Univ Fac Comp Sci & Engn Tehran Iran Shahid Beheshti Univ Med Sci Anesthesiol Res Ctr Tehran Iran Univ Leeds Ctr Computat Imaging & Simulat Technol Biomed CIS Sch Comp Leeds W Yorkshire England Royal Adelaide Hosp Dept Med Phys Adelaide SA Australia Univ Adelaide Sch Phys Sci Adelaide SA Australia Univ Tehran Med Sci Quantitat MR Imaging & Spect Grp QMISG Tehran Iran 

出 版 物：《COMPUTERS IN BIOLOGY AND MEDICINE》 (生物学与医学中的计算机)

年 卷 期：2021年第135卷

页      面：104605-104605页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 07[理学] 09[农学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：UK Research and Innovation  UKRI  (104425) 

主　　题：Computed tomography Corona virus COVID-19 Deep learning Machine learning Medical image computing Medical imaging 

摘      要：Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learningbased methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.