A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging.

ABSTRACT: Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning models to classify the TSS. We also propose a deep-learning model to extract hidden representations for the MR perfusion-weighted images and demonstrate classification im

SUBMITTER: Ho KC 

PROVIDER: S-EPMC6661120 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature