The Role of Imaging in the Detection and Management of COVID-19: A Review

By Di Dong, Zhenchao Tang, Shuo Wang, Hui Hui, Lixin Gong, Yao Lu, Zhonglin Xue, H. Liao, Fang Chen, Fan Yang and 9 more. IEEE Reviews in Biomedical Engineering, 2020

318 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/e49a05b8c45411a5e33c6e4ea74bbac6433c42e2

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In Biomedical Engineering

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound (2020, background): “In the wake of the current pandemic, recent works have focused on the detection of COVID-19 from chest CT [25], [26].”
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis (2020, unknown): “There are already some published studies on CT-based COVID-19 diagnosis systems 22,23 .”
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020, methodology): “s. Several reviews 1 The developed code is available at https://github.com/asmaa4may/DeTraC COVId19. 4 Asmaa Abbas et al. have been carried out to highlight recent contributions to COVID-19 detection [8,15,24]. For example, in [33], a CNN was applied based on Inception network to detect COVID-19 dis”
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images (2020, methodology): “y enlarging the training set with unlabeled data. C. Artiﬁcial Intelligence for COVID-19 Artiﬁcial intelligence technologies have been employed in a large number of applications against COVID-19 [6], [46]– [48]. Joseph et al. [15] categorized these applications into three scales, including patient s”
Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, unknown): “1 .”