Validation of a machine learning software tool for automated large vessel occlusion detection in patients with suspected acute stroke

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F22%3A00076315" target="_blank" >RIV/00159816:_____/22:00076315 - isvavai.cz</a>

Alternative codes found

RIV/00216224:14110/22:00128327

Result on the web

<a href="https://link.springer.com/article/10.1007/s00234-022-02978-x" target="_blank" >https://link.springer.com/article/10.1007/s00234-022-02978-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00234-022-02978-x" target="_blank" >10.1007/s00234-022-02978-x</a>

Result language

angličtina

Original language name

Validation of a machine learning software tool for automated large vessel occlusion detection in patients with suspected acute stroke

Original language description

Purpose CT angiography (CTA) is the imaging standard for large vessel occlusion (LVO) detection in patients with acute ischemic stroke. StrokeSENS LVO is an automated tool that utilizes a machine learning algorithm to identify anterior large vessel occlusions (LVO) on CTA. The aim of this study was to test the algorithm&apos;s performance in LVO detection in an independent dataset. Methods A total of 400 studies (217 LVO, 183 other/no occlusion) read by expert consensus were used for retrospective analysis. The LVO was defined as intracranial internal carotid artery (ICA) occlusion and M1 middle cerebral artery (MCA) occlusion. Software performance in detecting anterior LVO was evaluated using receiver operator characteristics (ROC) analysis, reporting area under the curve (AUC), sensitivity, and specificity. Subgroup analyses were performed to evaluate if performance in detecting LVO differed by subgroups, namely M1 MCA and ICA occlusion sites, and in data stratified by patient age, sex, and CTA acquisition characteristics (slice thickness, kilovoltage tube peak, and scanner manufacturer). Results AUC, sensitivity, and specificity overall were as follows: 0.939, 0.894, and 0.874, respectively, in the full cohort; 0.927, 0.857, and 0.874, respectively, in the ICA occlusion cohort; 0.945, 0.914, and 0.874, respectively, in the M1 MCA occlusion cohort. Performance did not differ significantly by patient age, sex, or CTA acquisition characteristics. Conclusion The StrokeSENS LVO machine learning algorithm detects anterior LVO with high accuracy from a range of scans in a large dataset.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30100 - Basic medicine

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Neuroradiology

ISSN

0028-3940

e-ISSN

1432-1920

Volume of the periodical

64

Issue of the periodical within the volume

12

Country of publishing house

US - UNITED STATES

Number of pages

11

Pages from-to

2245-2255

UT code for WoS article

000800996700001

EID of the result in the Scopus database

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