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A comprehensive review of imaging findings in COVID-19 - status in early 2021
Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view h...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087891/ https://www.ncbi.nlm.nih.gov/pubmed/33932183 http://dx.doi.org/10.1007/s00259-021-05375-3 |
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author | Afshar-Oromieh, Ali Prosch, Helmut Schaefer-Prokop, Cornelia Bohn, Karl Peter Alberts, Ian Mingels, Clemens Thurnher, Majda Cumming, Paul Shi, Kuangyu Peters, Alan Geleff, Silvana Lan, Xiaoli Wang, Feng Huber, Adrian Gräni, Christoph Heverhagen, Johannes T. Rominger, Axel Fontanellaz, Matthias Schöder, Heiko Christe, Andreas Mougiakakou, Stavroula Ebner, Lukas |
author_facet | Afshar-Oromieh, Ali Prosch, Helmut Schaefer-Prokop, Cornelia Bohn, Karl Peter Alberts, Ian Mingels, Clemens Thurnher, Majda Cumming, Paul Shi, Kuangyu Peters, Alan Geleff, Silvana Lan, Xiaoli Wang, Feng Huber, Adrian Gräni, Christoph Heverhagen, Johannes T. Rominger, Axel Fontanellaz, Matthias Schöder, Heiko Christe, Andreas Mougiakakou, Stavroula Ebner, Lukas |
author_sort | Afshar-Oromieh, Ali |
collection | PubMed |
description | Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [(18)F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners. |
format | Online Article Text |
id | pubmed-8087891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-80878912021-05-03 A comprehensive review of imaging findings in COVID-19 - status in early 2021 Afshar-Oromieh, Ali Prosch, Helmut Schaefer-Prokop, Cornelia Bohn, Karl Peter Alberts, Ian Mingels, Clemens Thurnher, Majda Cumming, Paul Shi, Kuangyu Peters, Alan Geleff, Silvana Lan, Xiaoli Wang, Feng Huber, Adrian Gräni, Christoph Heverhagen, Johannes T. Rominger, Axel Fontanellaz, Matthias Schöder, Heiko Christe, Andreas Mougiakakou, Stavroula Ebner, Lukas Eur J Nucl Med Mol Imaging Review Article Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [(18)F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners. Springer Berlin Heidelberg 2021-05-01 2021 /pmc/articles/PMC8087891/ /pubmed/33932183 http://dx.doi.org/10.1007/s00259-021-05375-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Review Article Afshar-Oromieh, Ali Prosch, Helmut Schaefer-Prokop, Cornelia Bohn, Karl Peter Alberts, Ian Mingels, Clemens Thurnher, Majda Cumming, Paul Shi, Kuangyu Peters, Alan Geleff, Silvana Lan, Xiaoli Wang, Feng Huber, Adrian Gräni, Christoph Heverhagen, Johannes T. Rominger, Axel Fontanellaz, Matthias Schöder, Heiko Christe, Andreas Mougiakakou, Stavroula Ebner, Lukas A comprehensive review of imaging findings in COVID-19 - status in early 2021 |
title | A comprehensive review of imaging findings in COVID-19 - status in early 2021 |
title_full | A comprehensive review of imaging findings in COVID-19 - status in early 2021 |
title_fullStr | A comprehensive review of imaging findings in COVID-19 - status in early 2021 |
title_full_unstemmed | A comprehensive review of imaging findings in COVID-19 - status in early 2021 |
title_short | A comprehensive review of imaging findings in COVID-19 - status in early 2021 |
title_sort | comprehensive review of imaging findings in covid-19 - status in early 2021 |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8087891/ https://www.ncbi.nlm.nih.gov/pubmed/33932183 http://dx.doi.org/10.1007/s00259-021-05375-3 |
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