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

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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Bibliographic Details
Main Authors: Carina Gleißner, Stephan Kaczmarz, Jan Kufer, Lena Schmitzer, Michael Kallmayer, Claus Zimmer, Benedikt Wiestler, Christine Preibisch, Jens Göttler
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-01-01
Series:Frontiers in Neuroimaging
Subjects:
asymptomatic carotid artery stenosis
hemodynamics
random forest–ensemble classifier
machine learning
individual watershed areas
magnetic resonance imaging (MRI)
Online Access:https://www.frontiersin.org/articles/10.3389/fnimg.2022.1056503/full

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https://www.frontiersin.org/articles/10.3389/fnimg.2022.1056503/full

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