Predicting Clinical Outcomes in Acute Ischemic Stroke Patients Undergoing Endovascular Thrombectomy with Machine Learning : A Systematic Review and Meta-analysis.

Yao Hao TeoIsis Claire Z Y LimFan Shuen TsengYao Neng TeoCheryl Shumin KowZi Hui Celeste NgNyein Chan Ko KoChing-Hui SiaAloysius S T LeowWesley YeungWan Yee KongBernard P L ChanVijay K SharmaLeonard Leong-Litt YeoBenjamin Y Q Tan

Published in: Clinical neuroradiology (2021)

ML may be useful as an adjunct to clinical assessment to predict functional outcomes in AIS patients undergoing thrombectomy, and hence identify suitable patients for treatment. Further studies validating ML models in large multicenter cohorts are necessary to explore this further.

Keyphrases

patients undergoing
machine learning
end stage renal disease
acute ischemic stroke
ejection fraction
newly diagnosed
chronic kidney disease
peritoneal dialysis
clinical trial
artificial intelligence
cross sectional
double blind
patient reported