Development and application of explainable artificial intelligence using machine learning classification for long-term facial nerve function after vestibular schwannoma surgery.

Journal: Journal of neuro-oncology
PMID: 39392590

Abstract

PURPOSE: Vestibular schwannomas (VSs) represent the most common cerebellopontine angle tumors, posing a challenge in preserving facial nerve (FN) function during surgery. We employed the Extreme Gradient Boosting machine learning classifier to predict long-term FN outcomes (classified as House-Brackmann grades 1-2 for good outcomes and 3-6 for bad outcomes) after VS surgery.

Authors

  • Lukasz Przepiorka
    Department of Neurosurgery, Medical University of Warsaw, Banacha St. 1a, 02-097, Warsaw, Poland.
  • Sławomir Kujawski
    Department of Exercise Physiology and Functional Anatomy, Ludwik Rydygier Collegium Medicum in Bydgoszcz Nicolaus Copernicus University in Toruń, Świętojańska 20, 85-077, Bydgoszcz, Poland. skujawski@cm.umk.pl.
  • Katarzyna Wójtowicz
    Department of Neurosurgery, Medical University of Warsaw, Banacha St. 1a, 02-097, Warsaw, Poland.
  • Edyta Maj
    Second Department of Radiology, Medical University of Warsaw, Banacha St. 1a, 02-097, Warsaw, Poland.
  • Andrzej Marchel
    Department of Neurosurgery, Medical University of Warsaw, Banacha St. 1a, 02-097, Warsaw, Poland.
  • Przemysław Kunert
    Department of Neurosurgery, Medical University of Warsaw, Banacha St. 1a, 02-097, Warsaw, Poland.

Keywords

External Resources

Popular Topics
Recent Journals