Research advances in machine learning for prognosis and risk of adverse event prediction after mechanical thrombectomy in acute anterior circulation large vessel occlusion
Li Chenwei
Yang Keke
Wang Xiaojun
Guo Weihua
Feng Zhiheng
Peng Huiyuan
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.
Keywords：Acute ischemic strokeThrombectomyMachine learningPrognosis predictionReview
Publication Date：2025-03-18
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：8( 210-216,后插1 )