Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.
COVID-19
artificial intelligence
lung
radiomics
tomography (X-ray computed)
Journal
Tomography (Ann Arbor, Mich.)
ISSN: 2379-139X
Titre abrégé: Tomography
Pays: Switzerland
ID NLM: 101671170
Informations de publication
Date de publication:
25 11 2022
25 11 2022
Historique:
received:
19
10
2022
revised:
19
11
2022
accepted:
21
11
2022
entrez:
22
12
2022
pubmed:
23
12
2022
medline:
27
12
2022
Statut:
epublish
Résumé
Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (
Identifiants
pubmed: 36548527
pii: tomography8060235
doi: 10.3390/tomography8060235
pmc: PMC9785796
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2815-2827Références
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