Charting the potential of brain computed tomography deep learning systems.

Buchlak, QD; Milne, MR; Seah, J; Johnson, A; Samarasinghe, G; Hachey, B; Esmaili, N; Tran, A; Leveque, J-C; Farrokhi, F; Goldschlager, T; Edelstein, S; Brotchie, P

Charting the potential of brain computed tomography deep learning systems.

Buchlak, QD Milne, MR Seah, J Johnson, A Samarasinghe, G Hachey, B Esmaili, N Tran, A Leveque, J-C Farrokhi, F Goldschlager, T Edelstein, S Brotchie, P

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: J Clin Neurosci, 2022, 99, pp. 217-223
Issue Date:: 2022-05

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Field	Value	Language
dc.contributor.author	Buchlak, QD
dc.contributor.author	Milne, MR
dc.contributor.author	Seah, J
dc.contributor.author	Johnson, A
dc.contributor.author	Samarasinghe, G
dc.contributor.author	Hachey, B
dc.contributor.author	Esmaili, N
dc.contributor.author	Tran, A
dc.contributor.author	Leveque, J-C
dc.contributor.author	Farrokhi, F
dc.contributor.author	Goldschlager, T
dc.contributor.author	Edelstein, S
dc.contributor.author	Brotchie, P
dc.date.accessioned	2023-06-29T02:52:44Z
dc.date.available	2022-03-08
dc.date.available	2023-06-29T02:52:44Z
dc.date.issued	2022-05
dc.identifier.citation	J Clin Neurosci, 2022, 99, pp. 217-223
dc.identifier.issn	0967-5868
dc.identifier.issn	1532-2653
dc.identifier.uri	http://hdl.handle.net/10453/170982
dc.description.abstract	Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients. Deep learning has shown promise for facilitating improved diagnostic accuracy and triage. This research charts the potential of deep learning applied to the analysis of CTB scans. It draws on the experience of practicing clinicians and technologists involved in development and implementation of deep learning-based clinical decision support systems. We consider the past, present and future of the CTB, along with limitations of existing systems as well as untapped beneficial use cases. Implementing deep learning CTB interpretation systems and effectively navigating development and implementation risks can deliver many benefits to clinicians and patients, ultimately improving efficiency and safety in healthcare.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	J Clin Neurosci
dc.relation.isbasedon	10.1016/j.jocn.2022.03.014
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.rights	This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
dc.subject	1103 Clinical Sciences, 1109 Neurosciences
dc.subject.classification	Neurology & Neurosurgery
dc.subject.mesh	Decision Support Systems, Clinical
dc.subject.mesh	Deep Learning
dc.subject.mesh	Humans
dc.subject.mesh	Neuroimaging
dc.subject.mesh	Tomography, X-Ray Computed
dc.subject.mesh	Humans
dc.subject.mesh	Tomography, X-Ray Computed
dc.subject.mesh	Decision Support Systems, Clinical
dc.subject.mesh	Neuroimaging
dc.subject.mesh	Deep Learning
dc.subject.mesh	Decision Support Systems, Clinical
dc.subject.mesh	Deep Learning
dc.subject.mesh	Humans
dc.subject.mesh	Neuroimaging
dc.subject.mesh	Tomography, X-Ray Computed
dc.title	Charting the potential of brain computed tomography deep learning systems.
dc.type	Journal Article
utslib.citation.volume	99
utslib.location.activity	Scotland
utslib.for	1103 Clinical Sciences
utslib.for	1109 Neurosciences
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	2023-06-29T02:52:43Z
pubs.publication-status	Published
pubs.volume	99

Abstract:

Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and mortality implications for patients. Deep learning has shown promise for facilitating improved diagnostic accuracy and triage. This research charts the potential of deep learning applied to the analysis of CTB scans. It draws on the experience of practicing clinicians and technologists involved in development and implementation of deep learning-based clinical decision support systems. We consider the past, present and future of the CTB, along with limitations of existing systems as well as untapped beneficial use cases. Implementing deep learning CTB interpretation systems and effectively navigating development and implementation risks can deliver many benefits to clinicians and patients, ultimately improving efficiency and safety in healthcare.

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

http://hdl.handle.net/10453/170982