Machine learning volumetry of ischemic brain lesions on CT after thrombectomy-prospective diagnostic accuracy study in ischemic stroke patients

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17110%2F20%3AA21026EF" target="_blank" >RIV/61988987:17110/20:A21026EF - isvavai.cz</a>
Alternative codes found
RIV/00216224:14110/20:00115700 RIV/00843989:_____/20:E0108530 RIV/00159816:_____/20:00073472
Result on the web
<a href="https://link.springer.com/article/10.1007/s00234-020-02419-7" target="_blank" >https://link.springer.com/article/10.1007/s00234-020-02419-7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00234-020-02419-7" target="_blank" >10.1007/s00234-020-02419-7</a>

Result language
angličtina
Original language name
Machine learning volumetry of ischemic brain lesions on CT after thrombectomy-prospective diagnostic accuracy study in ischemic stroke patients
Original language description
Ischemic lesion volume (ILV) is an important radiological predictor of functional outcome in patients with anterior circulation stroke. Our aim was to assess the agreement between automated ILV measurements on NCCT using the Brainomix software and manual ILV measurements on diffusion-weighted imaging (DWI).
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30210 - Clinical neurology

Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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