Every year, 13 million people suffer acute ischemic stroke. Brain tissue infarcts permanently within hours after stroke onset and rapid recanalization is therefore of utmost importance. In this project, we aim to estimate recanalization effect by a single convolutional neural network customized to include magnetic resonance imaging biomarkers as well as individual recanalization information. This is in contrast to the traditional approach which is splitting the data set according to the recanalization information and training several models. We find a significant recanalization effect and believe this to be an important step towards an automated decision support system.
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