Chest radiographs and machine learning - Past, present and future.

Jones, CM; Buchlak, QD; Oakden-Rayner, L; Milne, M; Seah, J; Esmaili, N; Hachey, B

Chest radiographs and machine learning - Past, present and future.

Jones, CM Buchlak, QD Oakden-Rayner, L Milne, M Seah, J Esmaili, N Hachey, B

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: J Med Imaging Radiat Oncol, 2021, 65, (5), pp. 538-544
Issue Date:: 2021-08

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Field	Value	Language
dc.contributor.author	Jones, CM
dc.contributor.author	Buchlak, QD
dc.contributor.author	Oakden-Rayner, L
dc.contributor.author	Milne, M
dc.contributor.author	Seah, J
dc.contributor.author	Esmaili, N
dc.contributor.author	Hachey, B
dc.date.accessioned	2022-05-12T04:09:31Z
dc.date.available	2021-06-08
dc.date.available	2022-05-12T04:09:31Z
dc.date.issued	2021-08
dc.identifier.citation	J Med Imaging Radiat Oncol, 2021, 65, (5), pp. 538-544
dc.identifier.issn	1754-9477
dc.identifier.issn	1754-9485
dc.identifier.uri	http://hdl.handle.net/10453/157274
dc.description.abstract	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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	WILEY
dc.relation.ispartof	J Med Imaging Radiat Oncol
dc.relation.isbasedon	10.1111/1754-9485.13274
dc.rights	This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	1103 Clinical Sciences, 1110 Nursing, 1112 Oncology and Carcinogenesis
dc.subject.classification	Nuclear Medicine & Medical Imaging
dc.subject.mesh	Humans
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Machine Learning
dc.subject.mesh	Radiography
dc.subject.mesh	Thorax
dc.subject.mesh	Thorax
dc.subject.mesh	Humans
dc.subject.mesh	Radiography
dc.subject.mesh	Image Processing, Computer-Assisted
dc.subject.mesh	Machine Learning
dc.title	Chest radiographs and machine learning - Past, present and future.
dc.type	Journal Article
utslib.citation.volume	65
utslib.location.activity	Australia
utslib.for	1103 Clinical Sciences
utslib.for	1110 Nursing
utslib.for	1112 Oncology and Carcinogenesis
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	recently_added	*
dc.date.updated	2022-05-12T04:09:28Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	65
utslib.citation.issue	5

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/157274