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Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?
Due to its widespread availability, low cost, feasibility at the patient’s bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to i...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858224/ https://www.ncbi.nlm.nih.gov/pubmed/36673027 http://dx.doi.org/10.3390/diagnostics13020216 |
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author | Irmici, Giovanni Cè, Maurizio Caloro, Elena Khenkina, Natallia Della Pepa, Gianmarco Ascenti, Velio Martinenghi, Carlo Papa, Sergio Oliva, Giancarlo Cellina, Michaela |
author_facet | Irmici, Giovanni Cè, Maurizio Caloro, Elena Khenkina, Natallia Della Pepa, Gianmarco Ascenti, Velio Martinenghi, Carlo Papa, Sergio Oliva, Giancarlo Cellina, Michaela |
author_sort | Irmici, Giovanni |
collection | PubMed |
description | Due to its widespread availability, low cost, feasibility at the patient’s bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion. |
format | Online Article Text |
id | pubmed-9858224 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-98582242023-01-21 Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? Irmici, Giovanni Cè, Maurizio Caloro, Elena Khenkina, Natallia Della Pepa, Gianmarco Ascenti, Velio Martinenghi, Carlo Papa, Sergio Oliva, Giancarlo Cellina, Michaela Diagnostics (Basel) Review Due to its widespread availability, low cost, feasibility at the patient’s bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion. MDPI 2023-01-06 /pmc/articles/PMC9858224/ /pubmed/36673027 http://dx.doi.org/10.3390/diagnostics13020216 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Irmici, Giovanni Cè, Maurizio Caloro, Elena Khenkina, Natallia Della Pepa, Gianmarco Ascenti, Velio Martinenghi, Carlo Papa, Sergio Oliva, Giancarlo Cellina, Michaela Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? |
title | Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? |
title_full | Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? |
title_fullStr | Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? |
title_full_unstemmed | Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? |
title_short | Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available? |
title_sort | chest x-ray in emergency radiology: what artificial intelligence applications are available? |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9858224/ https://www.ncbi.nlm.nih.gov/pubmed/36673027 http://dx.doi.org/10.3390/diagnostics13020216 |
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