Research advances in machine learning for prognosis and risk of adverse event prediction after mechanical thrombectomy in acute anterior circulation large vessel occlusion
10.3969/j.issn.1672-5921.2025.03.007
- VernacularTitle:机器学习在前循环急性大血管闭塞行机械取栓预后及不良事件发生风险预测中的应用进展
- Author:
Chenwei LI
1
;
Keke YANG
1
;
Xiaojun WANG
1
;
Weihua GUO
1
;
Zhiheng FENG
1
;
Huiyuan PENG
1
Author Information
1. 528400 中山市中医院(广州中医药大学附属中山中医院)神经内科
- Publication Type:Journal Article
- Keywords:
Acute ischemic stroke;
Thrombectomy;
Machine learning;
Prognosis prediction;
Review
- From:
Chinese Journal of Cerebrovascular Diseases
2025;22(3):210-216,后插1
- CountryChina
- Language:Chinese
-
Abstract:
Acute large vessel occlusion stroke(ALVOS)of anterior circulation is associated with severe clinical manifestations and high rates of disability and mortality.Mechanical thrombectomy has emerged as the primary therapeutic intervention.However,post-procedural outcomes remain highly variable,and patients continue to face elevated risks of poor prognosis.Machine learning,a transformative tool in medical research,enables comprehensive analysis of multimodal data to identify specific biomarkers and improve the accuracy of predictions for clinical outcomes and adverse events.This review summarized the latest developments in machine learning applications aim at predicting post-thrombectomy prognosis and risk of adverse event,including futile recanalization,hemorrhagic transformation,and malignant cerebral edema in patients with anterior circulation ALVOS in order to provide a basis for developing personalized treatment plan and improve their clinical prognosis.
