Deep learning survival model predicts outcome after intracerebral hemorrhage from initial CT scan.

Journal: European stroke journal
Published Date: Jun 16, 2024

Abstract

BACKGROUND: Predicting functional impairment after intracerebral hemorrhage (ICH) provides valuable information for planning of patient care and rehabilitation strategies. Current prognostic tools are limited in making long term predictions and require multiple expert-defined inputs and interpretation that make their clinical implementation challenging. This study aimed to predict long term functional impairment of ICH patients from admission non-contrast CT scans, leveraging deep learning models in a survival analysis framework.

Authors

  • Yutong Chen
    Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Cyprien A Rivier
    Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
  • Samantha A Mora
    Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Victor Torres Lopez
    Department of Neurology, Yale School of Medicine, New Haven, CT, USA.
  • Sam Payabvash
    Department of Radiology, Yale School of Medicine, New Haven, CT.
  • Kevin N Sheth
    Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
  • Andreas Harloff
    Department of Neurology and Neurophysiology, University Medical Center Freiburg - Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Guido J Falcone
    Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
  • Jonathan Rosand
    Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Ernst Mayerhofer
    Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
  • Christopher D Anderson
    Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts.

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