- 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
- 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
Actions
Authors
Bibliographic Details
Item Description
Terms of use
Metrics
Altmetrics
Dimensions
If you are the owner of this record, you can report an update to it here: Report update to this record