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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Hlavní autoři:	Carina Gleißner, Stephan Kaczmarz, Jan Kufer, Lena Schmitzer, Michael Kallmayer, Claus Zimmer, Benedikt Wiestler, Christine Preibisch, Jens Göttler
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Frontiers Media S.A. 2023-01-01
Edice:	Frontiers in Neuroimaging
Témata:	asymptomatic carotid artery stenosis hemodynamics random forest–ensemble classifier machine learning individual watershed areas magnetic resonance imaging (MRI)
On-line přístup:	https://www.frontiersin.org/articles/10.3389/fnimg.2022.1056503/full
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