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Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging
The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides va...
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/PMC7864619/ https://www.ncbi.nlm.nih.gov/pubmed/33585153 http://dx.doi.org/10.1007/s12553-021-00520-2 |
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author | López-Cabrera, José Daniel Orozco-Morales, Rubén Portal-Diaz, Jorge Armando Lovelle-Enríquez, Orlando Pérez-Díaz, Marlén |
author_facet | López-Cabrera, José Daniel Orozco-Morales, Rubén Portal-Diaz, Jorge Armando Lovelle-Enríquez, Orlando Pérez-Díaz, Marlén |
author_sort | López-Cabrera, José Daniel |
collection | PubMed |
description | The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results. |
format | Online Article Text |
id | pubmed-7864619 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-78646192021-02-09 Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging López-Cabrera, José Daniel Orozco-Morales, Rubén Portal-Diaz, Jorge Armando Lovelle-Enríquez, Orlando Pérez-Díaz, Marlén Health Technol (Berl) Original Paper The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results. Springer Berlin Heidelberg 2021-02-05 2021 /pmc/articles/PMC7864619/ /pubmed/33585153 http://dx.doi.org/10.1007/s12553-021-00520-2 Text en © IUPESM and Springer-Verlag GmbH Germany, part of Springer Nature 2021 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Paper López-Cabrera, José Daniel Orozco-Morales, Rubén Portal-Diaz, Jorge Armando Lovelle-Enríquez, Orlando Pérez-Díaz, Marlén Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging |
title | Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging |
title_full | Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging |
title_fullStr | Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging |
title_full_unstemmed | Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging |
title_short | Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging |
title_sort | current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7864619/ https://www.ncbi.nlm.nih.gov/pubmed/33585153 http://dx.doi.org/10.1007/s12553-021-00520-2 |
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