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Long-COVID diagnosis: From diagnostic to advanced AI-driven models
SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, li...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Published by Elsevier B.V.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791239/ https://www.ncbi.nlm.nih.gov/pubmed/35114535 http://dx.doi.org/10.1016/j.ejrad.2022.110164 |
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author | Cau, Riccardo Faa, Gavino Nardi, Valentina Balestrieri, Antonella Puig, Josep Suri, Jasjit S SanFilippo, Roberto Saba, Luca |
author_facet | Cau, Riccardo Faa, Gavino Nardi, Valentina Balestrieri, Antonella Puig, Josep Suri, Jasjit S SanFilippo, Roberto Saba, Luca |
author_sort | Cau, Riccardo |
collection | PubMed |
description | SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”. Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have. |
format | Online Article Text |
id | pubmed-8791239 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Published by Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87912392022-01-27 Long-COVID diagnosis: From diagnostic to advanced AI-driven models Cau, Riccardo Faa, Gavino Nardi, Valentina Balestrieri, Antonella Puig, Josep Suri, Jasjit S SanFilippo, Roberto Saba, Luca Eur J Radiol Article SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely. Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”. Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes. In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit. The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have. Published by Elsevier B.V. 2022-03 2022-01-19 /pmc/articles/PMC8791239/ /pubmed/35114535 http://dx.doi.org/10.1016/j.ejrad.2022.110164 Text en © 2022 Published by Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Cau, Riccardo Faa, Gavino Nardi, Valentina Balestrieri, Antonella Puig, Josep Suri, Jasjit S SanFilippo, Roberto Saba, Luca Long-COVID diagnosis: From diagnostic to advanced AI-driven models |
title | Long-COVID diagnosis: From diagnostic to advanced AI-driven
models |
title_full | Long-COVID diagnosis: From diagnostic to advanced AI-driven
models |
title_fullStr | Long-COVID diagnosis: From diagnostic to advanced AI-driven
models |
title_full_unstemmed | Long-COVID diagnosis: From diagnostic to advanced AI-driven
models |
title_short | Long-COVID diagnosis: From diagnostic to advanced AI-driven
models |
title_sort | long-covid diagnosis: from diagnostic to advanced ai-driven
models |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8791239/ https://www.ncbi.nlm.nih.gov/pubmed/35114535 http://dx.doi.org/10.1016/j.ejrad.2022.110164 |
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