IEEE transactions on bio-medical engineering
29989941
OBJECTIVE: Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurr...
BACKGROUND: Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and...
BACKGROUND AND PURPOSE: In acute stroke patients with large vessel occlusions, it would be helpful to be able to predict the difference in the size and location of the final infarct based on the outcome of reperfusion therapy. Our aim was to demonstr...
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
33887664
INTRODUCTION: Novel machine learning (ML) methods are being investigated across medicine for their predictive capabilities while boasting increased adaptability and generalizability. In our study, we compare logistic regression with machine learning ...
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
34159824
Machine Learning (ML) has been proposed for tissue fate prediction after acute ischemic stroke (AIS), with the aim to help treatment decision and patient management. We compared three different ML models to the clinical method based on diffusion-perf...
RATIONALE AND OBJECTIVES: Hemorrhagic transformation (HT) is one of the most serious complications in patients with acute ischemic stroke (AIS) following reperfusion therapy. The purpose of this study is to develop and validate deep learning (DL) mod...