Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy

Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy

ObjectiveTo investigate whether intracranial artery calcification (IAC) serves as a reliable imaging predictor of mechanical thrombectomy (MT) outcomes and to develop robust machine learning (ML) models incorporating preoperative emergency data to predict outcomes in patients with acute ischemic str...

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Bibliographic Details
Main Authors: Guangzong Li, Yuesen Zhang, Di Li, Manhong Zhao, Lin Yin
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-08-01
Series:Frontiers in Neurology
Subjects:
intracranial artery calcification
mechanical thrombectomy
ischemic stroke
artificial intelligence
machine learning
Online Access:https://www.frontiersin.org/articles/10.3389/fneur.2025.1642807/full
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