- Abstract:
-
Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more likely to have good outcomes and ranges from 0 (no collaterals) to 3 (complete collaterals). In this study, we used a machine learning approach to categorise the degree of collateral flow in 98 patients who were eligible for mechanical thrombectomy and generate an e-CTA collateral score (CTA-CS) for each patient (e-STROKE SUITE, Brainomix Ltd., Oxfo...
Expand abstract - Publication status:
- Published
- Peer review status:
- Peer reviewed
- Files:
-
- Publisher copy:
- 10.1159/000500076
-
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- Publisher:
- Karger Publishers Publisher's website
- Journal:
- Cerebrovascular Diseases Journal website
- Volume:
- 47
- Issue:
- 5-6
- Pages:
- 217–222
- Publication date:
- 2019-06-19
- Acceptance date:
- 2019-03-28
- DOI:
- EISSN:
-
1421-9786
- ISSN:
-
1015-9770
- Pubs id:
-
pubs:1020171
- UUID:
-
uuid:88289ed6-418e-44ec-ac1e-27f601ec4c1e
- Source identifiers:
-
1020171
- Local pid:
- pubs:1020171
- Copyright holder:
- Grunwald, IQ et al
- Rights statement:
- © 2019 The Author(s). Published by S. Karger AG, Basel. This article is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND). Usage and distribution for commercial purposes as well as any distribution of modified material requires written permission.
Journal article
Collateral automation for triage in stroke: evaluating automated scoring of collaterals in acute stroke on computed tomography scans
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