SARS-CoV-2 diagnosis using medical imaging techniques and artificial intelligence: A review.
Artificial intelligence
Chest CT
Clinical findings
Medical imaging techniques
SARS-CoV-2
Journal
Clinical imaging
ISSN: 1873-4499
Titre abrégé: Clin Imaging
Pays: United States
ID NLM: 8911831
Informations de publication
Date de publication:
Aug 2021
Aug 2021
Historique:
received:
09
07
2020
revised:
30
12
2020
accepted:
17
01
2021
pubmed:
6
2
2021
medline:
23
6
2021
entrez:
5
2
2021
Statut:
ppublish
Résumé
SARS-CoV-2 is a worldwide health emergency with unrecognized clinical features. This paper aims to review the most recent medical imaging techniques used for the diagnosis of SARS-CoV-2 and their potential contributions to attenuate the pandemic. Recent researches, including artificial intelligence tools, will be described. We review the main clinical features of SARS-CoV-2 revealed by different medical imaging techniques. First, we present the clinical findings of each technique. Then, we describe several artificial intelligence approaches introduced for the SARS-CoV-2 diagnosis. CT is the most accurate diagnostic modality of SARS-CoV-2. Additionally, ground-glass opacities and consolidation are the most common signs of SARS-CoV-2 in CT images. However, other findings such as reticular pattern, and crazy paving could be observed. We also found that pleural effusion and pneumothorax features are less common in SARS-CoV-2. According to the literature, the B lines artifacts and pleural line irregularities are the common signs of SARS-CoV-2 in ultrasound images. We have also stated the different studies, focusing on artificial intelligence tools, to evaluate the SARS-CoV-2 severity. We found that most of the reported works based on deep learning focused on the detection of SARS-CoV-2 from medical images while the challenge for the radiologists is how to differentiate between SARS-CoV-2 and other viral infections with the same clinical features. The identification of SARS-CoV-2 manifestations on medical images is a key step in radiological workflow for the diagnosis of the virus and could be useful for researchers working on computer-aided diagnosis of pulmonary infections.
Identifiants
pubmed: 33545517
pii: S0899-7071(21)00024-3
doi: 10.1016/j.clinimag.2021.01.019
pmc: PMC7840409
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
6-14Commentaires et corrections
Type : CommentIn
Type : CommentIn
Informations de copyright
Copyright © 2021 Elsevier Inc. All rights reserved.
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