Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.

Journal: Stroke
Published Date: Jun 1, 2018

Abstract

BACKGROUND AND PURPOSE: Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume.

Authors

  • Anne Nielsen
    From the Department of Clinical Medicine, Center of Functionally Integrative Neuroscience and MINDLAB, Aarhus University, Denmark (A.N., M.B.H., A.T., K.M.) anne@cfin.au.dk.
  • Mikkel Bo Hansen
    From the Department of Clinical Medicine, Center of Functionally Integrative Neuroscience and MINDLAB, Aarhus University, Denmark (A.N., M.B.H., A.T., K.M.).
  • Anna Tietze
    From the Department of Clinical Medicine, Center of Functionally Integrative Neuroscience and MINDLAB, Aarhus University, Denmark (A.N., M.B.H., A.T., K.M.).
  • Kim Mouridsen
    From the Department of Clinical Medicine, Center of Functionally Integrative Neuroscience and MINDLAB, Aarhus University, Denmark (A.N., M.B.H., A.T., K.M.).

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