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The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic
Artificial intelligence (AI) plays a crucial role in the future development of all healthcare sectors ranging from clinical assistance of physicians by providing accurate diagnosis, prognosis and treatment to the development of vaccinations and aiding in the combat against the Covid-19 global pandem...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The British Institute of Radiology.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459850/ https://www.ncbi.nlm.nih.gov/pubmed/36105414 http://dx.doi.org/10.1259/bjro.20210075 |
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author | AlNuaimi, Dana AlKetbi, Reem |
author_facet | AlNuaimi, Dana AlKetbi, Reem |
author_sort | AlNuaimi, Dana |
collection | PubMed |
description | Artificial intelligence (AI) plays a crucial role in the future development of all healthcare sectors ranging from clinical assistance of physicians by providing accurate diagnosis, prognosis and treatment to the development of vaccinations and aiding in the combat against the Covid-19 global pandemic. AI has an important role in diagnostic radiology where the algorithms can be trained by large datasets to accurately provide a timely diagnosis of the radiological images given. This has led to the development of several AI algorithms that can be used in regions of scarcity of radiologists during the current pandemic by simply denoting the presence or absence of Covid-19 pneumonia in PCR positive patients on plain chest radiographs as well as in helping to levitate the over-burdened radiology departments by accelerating the time for report delivery. Plain chest radiography is the most common radiological study in the emergency department setting and is readily available, fast and a cheap method that can be used in triaging patients as well as being portable in the medical wards and can be used as the initial radiological examination in Covid-19 positive patients to detect pneumonic changes. Numerous studies have been done comparing several AI algorithms to that of experienced thoracic radiologists in plain chest radiograph reports measuring accuracy of each in Covid-19 patients. The majority of studies have reported performance equal or higher to that of the well-experienced thoracic radiologist in predicting the presence or absence of Covid-19 pneumonic changes in the provided chest radiographs. |
format | Online Article Text |
id | pubmed-9459850 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The British Institute of Radiology. |
record_format | MEDLINE/PubMed |
spelling | pubmed-94598502022-09-13 The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic AlNuaimi, Dana AlKetbi, Reem BJR Open Review Article Artificial intelligence (AI) plays a crucial role in the future development of all healthcare sectors ranging from clinical assistance of physicians by providing accurate diagnosis, prognosis and treatment to the development of vaccinations and aiding in the combat against the Covid-19 global pandemic. AI has an important role in diagnostic radiology where the algorithms can be trained by large datasets to accurately provide a timely diagnosis of the radiological images given. This has led to the development of several AI algorithms that can be used in regions of scarcity of radiologists during the current pandemic by simply denoting the presence or absence of Covid-19 pneumonia in PCR positive patients on plain chest radiographs as well as in helping to levitate the over-burdened radiology departments by accelerating the time for report delivery. Plain chest radiography is the most common radiological study in the emergency department setting and is readily available, fast and a cheap method that can be used in triaging patients as well as being portable in the medical wards and can be used as the initial radiological examination in Covid-19 positive patients to detect pneumonic changes. Numerous studies have been done comparing several AI algorithms to that of experienced thoracic radiologists in plain chest radiograph reports measuring accuracy of each in Covid-19 patients. The majority of studies have reported performance equal or higher to that of the well-experienced thoracic radiologist in predicting the presence or absence of Covid-19 pneumonic changes in the provided chest radiographs. The British Institute of Radiology. 2022-05-26 /pmc/articles/PMC9459850/ /pubmed/36105414 http://dx.doi.org/10.1259/bjro.20210075 Text en © 2022 The Authors. Published by the British Institute of Radiology https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Review Article AlNuaimi, Dana AlKetbi, Reem The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic |
title | The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic |
title_full | The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic |
title_fullStr | The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic |
title_full_unstemmed | The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic |
title_short | The role of artificial intelligence in plain chest radiographs interpretation during the Covid-19 pandemic |
title_sort | role of artificial intelligence in plain chest radiographs interpretation during the covid-19 pandemic |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9459850/ https://www.ncbi.nlm.nih.gov/pubmed/36105414 http://dx.doi.org/10.1259/bjro.20210075 |
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