Development of machine learning-based preoperative predictive analytics for unruptured intracranial aneurysm surgery: a pilot study.

Journal: Acta neurochirurgica
Published Date: May 1, 2020

Abstract

BACKGROUND: The decision to treat unruptured intracranial aneurysms (UIAs) or not is complex and requires balancing of risk factors and scores. Machine learning (ML) algorithms have previously been effective at generating highly accurate and comprehensive individualized preoperative predictive analytics in transsphenoidal pituitary and open tumor surgery. In this pilot study, we evaluate whether ML-based prediction of clinical endpoints is feasible for microsurgical management of UIAs.

Authors

  • Victor E Staartjes
    Department of Neurosurgery, Bergman Clinics, Naarden, The Netherlands; and.
  • Martina Sebök
    Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Patricia G Blum
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Carlo Serra
    1Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Switzerland.
  • Menno R Germans
    Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland.
  • Niklaus Krayenbühl
    Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland.
  • Luca Regli
    Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
  • Giuseppe Esposito
    Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

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