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Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article
In the present study, a particular technique of artificial intelligence (AI) is applied for diagnosis and classifying medical images of patients with coronavirus disease (COVID-19). Chest radiography and laboratory-based tests are two of the most important diagnostic approaches for the detection of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer - Medknow
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9480792/ https://www.ncbi.nlm.nih.gov/pubmed/36119307 http://dx.doi.org/10.4103/jfmpc.jfmpc_1715_21 |
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author | Yadollahi, Sepideh Yadollahi, Setareh Zanjani, Elmira Khaleghi, Fatemeh |
author_facet | Yadollahi, Sepideh Yadollahi, Setareh Zanjani, Elmira Khaleghi, Fatemeh |
author_sort | Yadollahi, Sepideh |
collection | PubMed |
description | In the present study, a particular technique of artificial intelligence (AI) is applied for diagnosis and classifying medical images of patients with coronavirus disease (COVID-19). Chest radiography and laboratory-based tests are two of the most important diagnostic approaches for the detection of people with the coronavirus. Recently, a lot of studies have been carried out on using AI techniques for achieving appropriate diagnosis of COVID-19 patients using computed tomography (CT) of the chest. The present study is reviewing all available literature that have investigated the role of chest CT toward AI in the detection of COVID-19. As a novel field of computer science, AI focuses on teaching computers to be capable of learning complex tasks and decide about their solution methods. In this study, we used Matlab, Payton, and Fortran software as well as other software which are suitable for this research. In this regard, the present review study is aimed to collect the information from all the studies conducted on the role of AI as a decisive and comprehensive technology for the detection of coronavirus in patients to have a more accurate diagnosis and investigate its epidemiology. |
format | Online Article Text |
id | pubmed-9480792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Wolters Kluwer - Medknow |
record_format | MEDLINE/PubMed |
spelling | pubmed-94807922022-09-17 Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article Yadollahi, Sepideh Yadollahi, Setareh Zanjani, Elmira Khaleghi, Fatemeh J Family Med Prim Care Review Article In the present study, a particular technique of artificial intelligence (AI) is applied for diagnosis and classifying medical images of patients with coronavirus disease (COVID-19). Chest radiography and laboratory-based tests are two of the most important diagnostic approaches for the detection of people with the coronavirus. Recently, a lot of studies have been carried out on using AI techniques for achieving appropriate diagnosis of COVID-19 patients using computed tomography (CT) of the chest. The present study is reviewing all available literature that have investigated the role of chest CT toward AI in the detection of COVID-19. As a novel field of computer science, AI focuses on teaching computers to be capable of learning complex tasks and decide about their solution methods. In this study, we used Matlab, Payton, and Fortran software as well as other software which are suitable for this research. In this regard, the present review study is aimed to collect the information from all the studies conducted on the role of AI as a decisive and comprehensive technology for the detection of coronavirus in patients to have a more accurate diagnosis and investigate its epidemiology. Wolters Kluwer - Medknow 2022-06 2022-06-30 /pmc/articles/PMC9480792/ /pubmed/36119307 http://dx.doi.org/10.4103/jfmpc.jfmpc_1715_21 Text en Copyright: © 2022 Journal of Family Medicine and Primary Care https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Review Article Yadollahi, Sepideh Yadollahi, Setareh Zanjani, Elmira Khaleghi, Fatemeh Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article |
title | Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article |
title_full | Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article |
title_fullStr | Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article |
title_full_unstemmed | Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article |
title_short | Application of machine learning and medical imaging in the detection of COVID-19 patients: A review article |
title_sort | application of machine learning and medical imaging in the detection of covid-19 patients: a review article |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9480792/ https://www.ncbi.nlm.nih.gov/pubmed/36119307 http://dx.doi.org/10.4103/jfmpc.jfmpc_1715_21 |
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