Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study.

Journal: Academic radiology
Published Date: Feb 1, 2020

Abstract

RATIONALE AND OBJECTIVES: Intravenous thrombolysis decision-making and obtaining of consent would be assisted by an individualized risk-benefit ratio. Deep learning (DL) models may be able to assist with this patient selection.

Authors

  • Stephen Bacchi
    Faculty of Health and Medical Sciences, Adelaide Medical School, University of Adelaide, Adelaide, SA 5000 Australia.
  • Toby Zerner
    Faculty of Health and Medical Sciences, University of Adelaide, Australia (T.Z., T.K., J.J.).
  • Luke Oakden-Rayner
    Department of Medical Imaging Research, Royal Adelaide Hospital, Adelaide, Australia.
  • Timothy Kleinig
    From the Royal Adelaide Hospital, Adelaide, Australia (S.B., L.O.-R., T.K., S.P., J.J.).
  • Sandy Patel
    From the Royal Adelaide Hospital, Adelaide, Australia (S.B., L.O.-R., T.K., S.P., J.J.).
  • Jim Jannes
    From the Royal Adelaide Hospital, Adelaide, Australia (S.B., L.O.-R., T.K., S.P., J.J.).

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