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

ABSTRACT:

Background

Internal 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 detect severely affected patients. We aimed to identify the most sensitive parameters and volumes of interest (VOI) to predict high-grade ICAS with random forest machine learning. We hypothesized an increased predictive ability considering iWSAs and a decreased cognitive performance in correctly classified patients.

Materials and methods

Twenty-four patients with asymptomatic, unilateral, high-grade carotid artery stenosis and 24 age-matched healthy controls underwent MRI comprisin

SUBMITTER: Gleißner C 

PROVIDER: S-EPMC10406220 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature