Hemodynamic MRI parameters to predict asymptomatic unilateral carotid artery stenosis with random forest machine learning

BackgroundInternal carotid artery stenosis (ICAS) can cause stroke and cognitive decline. Associated hemodynamic impairments, which are most pronounced within individual watershed areas (iWSA) between vascular territories, can be assessed with hemodynamic-oxygenation-sensitive MRI and may help to de...

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Detalhes bibliográficos

Principais autores:	Carina Gleißner, Stephan Kaczmarz, Jan Kufer, Lena Schmitzer, Michael Kallmayer, Claus Zimmer, Benedikt Wiestler, Christine Preibisch, Jens Göttler
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2023-01-01
coleção:	Frontiers in Neuroimaging
Assuntos:	asymptomatic carotid artery stenosis hemodynamics random forest–ensemble classifier machine learning individual watershed areas magnetic resonance imaging (MRI)
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/fnimg.2022.1056503/full
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