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Chest radiographs and machine learning – Past, present and future
Despite its simple acquisition technique, the chest X‐ray remains the most common first‐line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X‐ray interpretat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453538/ https://www.ncbi.nlm.nih.gov/pubmed/34169648 http://dx.doi.org/10.1111/1754-9485.13274 |
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author | Jones, Catherine M Buchlak, Quinlan D Oakden‐Rayner, Luke Milne, Michael Seah, Jarrel Esmaili, Nazanin Hachey, Ben |
author_facet | Jones, Catherine M Buchlak, Quinlan D Oakden‐Rayner, Luke Milne, Michael Seah, Jarrel Esmaili, Nazanin Hachey, Ben |
author_sort | Jones, Catherine M |
collection | PubMed |
description | Despite its simple acquisition technique, the chest X‐ray remains the most common first‐line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X‐ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well‐tested machine learning algorithms will be a revolution akin to early advances in X‐ray technology. Current use cases, strengths, limitations and applications of chest X‐ray machine learning systems are discussed. |
format | Online Article Text |
id | pubmed-8453538 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84535382021-09-27 Chest radiographs and machine learning – Past, present and future Jones, Catherine M Buchlak, Quinlan D Oakden‐Rayner, Luke Milne, Michael Seah, Jarrel Esmaili, Nazanin Hachey, Ben J Med Imaging Radiat Oncol MEDICAL IMAGING Despite its simple acquisition technique, the chest X‐ray remains the most common first‐line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X‐ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well‐tested machine learning algorithms will be a revolution akin to early advances in X‐ray technology. Current use cases, strengths, limitations and applications of chest X‐ray machine learning systems are discussed. John Wiley and Sons Inc. 2021-06-25 2021-08 /pmc/articles/PMC8453538/ /pubmed/34169648 http://dx.doi.org/10.1111/1754-9485.13274 Text en © 2021 Annalise-AI. Journal of Medical Imaging and Radiation Oncology published by John Wiley & Sons Australia, Ltd on behalf of Royal Australian and New Zealand College of Radiologists https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | MEDICAL IMAGING Jones, Catherine M Buchlak, Quinlan D Oakden‐Rayner, Luke Milne, Michael Seah, Jarrel Esmaili, Nazanin Hachey, Ben Chest radiographs and machine learning – Past, present and future |
title | Chest radiographs and machine learning – Past, present and future |
title_full | Chest radiographs and machine learning – Past, present and future |
title_fullStr | Chest radiographs and machine learning – Past, present and future |
title_full_unstemmed | Chest radiographs and machine learning – Past, present and future |
title_short | Chest radiographs and machine learning – Past, present and future |
title_sort | chest radiographs and machine learning – past, present and future |
topic | MEDICAL IMAGING |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8453538/ https://www.ncbi.nlm.nih.gov/pubmed/34169648 http://dx.doi.org/10.1111/1754-9485.13274 |
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