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Abstract #5665

Deep learning: Utilizing the potential in data bases to predict individual outcome in acute stroke

Anne Nielsen1,2, Kim Mouridsen1, Mikkel Bo Hansen1, and Jens Kjærgaard Boldsen1

1Center of Functionally Integrative Neuroscience and MINDLab, Institute of Clinical Medicine, Aarhus University, Aarhus C, Denmark, 2Combat Stroke, Aarhus C, Denmark

Acute ischemic stroke is a major disease and one of the leading causes of adult death and disability. Brain tissue infarcts permanently within hours after onset and rapid reperfusion treatment is therefore of utmost importance. Current methods to predict the tissue outcome are too simplistic. In this project a more advanced approach using deep neural networks to utilize the information from previous patient developments was established and compared to current state-of-the-art. The predictions from the deep neural networks were showed to be superior to the state-of-the-art method improving prediction accuracy and hence leading to better decision support.

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