| Literature DB >> 32356760 |
Di Dong, Zhenchao Tang, Shuo Wang, Hui Hui, Lixin Gong, Yao Lu, Zhong Xue, Hongen Liao, Fang Chen, Fan Yang, Ronghua Jin, Kun Wang, Zhenyu Liu, Jingwei Wei, Wei Mu, Hui Zhang, Jingying Jiang, Jie Tian, Hongjun Li.
Abstract
Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.Entities:
Mesh：
Year: 2021 PMID： 32356760 DOI： 10.1109/RBME.2020.2990959
Source DB: PubMed Journal: IEEE Rev Biomed Eng ISSN： 1937-3333