Machine learning for outcome prediction of neurosurgical aneurysm treatment: Current methods and future directions.

Journal: Clinical neurology and neurosurgery
Published Date:

Abstract

INTRODUCTION: Machine learning algorithms have received increased attention in neurosurgical literature for improved accuracy over traditional predictive methods. In this review, the authors sought to assess current applications of machine learning for outcome prediction of neurosurgical treatment of intracranial aneurysms and identify areas for future research.

Authors

  • Lohit Velagapudi
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
  • Fadi Al Saiegh
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Shreya Swaminathan
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA. Electronic address: Shreya.swaminathan@jefferson.edu.
  • Nikolaos Mouchtouris
    Department of Neurosurgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania.
  • Omaditya Khanna
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Victor Sabourin
    Department of Neurosurgery, Thomas Jefferson University Hospitals, Philadelphia, Pennsylvania.
  • M Reid Gooch
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Nabeel Herial
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Stavropoula Tjoumakaris
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Robert H Rosenwasser
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA.
  • Pascal Jabbour
    Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA. Electronic address: pascal.jabbour@jefferson.edu.