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
- 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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Full metadata record
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.
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