Improving Treatment Decisions for Acute Stroke Patients through Deep Learning based Risk Analysis

Despite recent success in treatment of acute ischemic stroke, treatment decisions and intervention planning remain difficult and time consuming. Alongside neurological symptoms complex image modalities have to be taken into account, such as magnetic resonance imaging and clinical data. In this project scientists from clinical neurology and data analysis collaborate to improve intervention planning and functional outcome prediction after acute ischemic stroke. The aim is not only prediction of functional outcome but discovery of underlying causal structures and risk factors that influence treatment success.