Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke.

Journal: AJNR. American journal of neuroradiology
PMID: 33766823

Abstract

BACKGROUND AND PURPOSE: In acute stroke patients with large vessel occlusions, it would be helpful to be able to predict the difference in the size and location of the final infarct based on the outcome of reperfusion therapy. Our aim was to demonstrate the value of deep learning-based tissue at risk and ischemic core estimation. We trained deep learning models using a baseline MR image in 3 multicenter trials.

Authors

  • Y Yu
  • Y Xie
    From the Department of Radiology, Neuroradiology Section (B.J., G.Z., Y.X., J.J.H., H.C., Y.L., G.Z., M.W.), Stanford University School of Medicine, Palo Alto, California.
  • T Thamm
    From the Radiology Department (Y.Y., Y.X., T.T., M.P.M., G.Z.), Stanford University, California.
  • E Gong
    Electrical Engineering (E.G.), Stanford University and Stanford University Medical Center, Stanford, California.
  • J Ouyang
    Electrical Engineering Department (E.G., J.O.), Stanford University, California.
  • S Christensen
    Neurology Department (S.C., M.G.L., G.W.A.), Stanford University, California.
  • M P Marks
    From the Radiology Department (Y.Y., Y.X., T.T., M.P.M., G.Z.), Stanford University, California.
  • M G Lansberg
    Neurology Department (S.C., M.G.L., G.W.A.), Stanford University, California.
  • G W Albers
    Neurology Department (S.C., M.G.L., G.W.A.), Stanford University, California.
  • G Zaharchuk
    From the Departments of Radiology (G.Z., M.W., D.R., C.P.L.) gregz@stanford.edu.

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