An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis

An interpretable machine learning model for stroke recurrence in patients with symptomatic intracranial atherosclerotic arterial stenosis

Background and objectiveSymptomatic intracranial atherosclerotic stenosis (SICAS) is the most common etiology of ischemic stroke and one of the main causes of high stroke recurrence. The recurrence of stroke is closely related to the prognosis of ischemic stroke. This study aims to develop a machine...

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Bibliographic Details
Main Authors: Yu Gao, Zi-ang Li, Xiao-yang Zhai, Lin Han, Ping Zhang, Si-jia Cheng, Jun-yan Yue, Hong-kai Cui
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Neuroscience
Subjects:
stroke recurrence
HR-VWI
machine learning
SICAS
plaque
Online Access:https://www.frontiersin.org/articles/10.3389/fnins.2023.1323270/full

Internet

https://www.frontiersin.org/articles/10.3389/fnins.2023.1323270/full

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