Machine learning for clinical outcome prediction in cerebrovascular and endovascular neurosurgery: systematic review and meta-analysis.

Journal: Journal of neurointerventional surgery
Published Date: May 22, 2025

Abstract

BACKGROUND: Machine learning (ML) may be superior to traditional methods for clinical outcome prediction. We sought to systematically review the literature on ML for clinical outcome prediction in cerebrovascular and endovascular neurosurgery.

Authors

  • Haydn Hoffman
    Department of Neurosurgery, University of California Los Angeles, 300 Stein Plaza, Suite 536, Los Angeles, CA 90095-6901, USA.
  • Jason J Sims
    The University of Tennessee Health Science Center, Memphis, Tennessee, USA.
  • Violiza Inoa-Acosta
    Semmes-Murphey Neurologic and Spine Institute, Memphis, Tennessee, USA.
  • Daniel Hoit
    Semmes Murphey Clinic, Memphis, Tennessee, USA; Department of Neurosurgery, The University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
  • Adam S Arthur
    Semmes Murphey Clinic, Memphis, Tennessee, USA; Department of Neurosurgery, The University of Tennessee Health Sciences Center, Memphis, Tennessee, USA.
  • Dan Y Draytsel
    SUNY Upstate Medical University, Syracuse, New York, USA.
  • YeonSoo Kim
    SUNY Upstate Medical University, Syracuse, New York, USA.
  • Nitin Goyal
    Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL, USA.

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