Predicting Spinal Surgery Candidacy From Imaging Data Using Machine Learning.

Journal: Neurosurgery
Published Date:

Abstract

BACKGROUND: The referral process for consultation with a spine surgeon remains inefficient, given a substantial proportion of referrals to spine surgeons are nonoperative.

Authors

  • Bayard Wilson
    Department of Neurosurgery, University of California, Los Angeles, United States.
  • Bilwaj Gaonkar
    Center for Biomedical Image Computing and Analytics, United States. Electronic address: bilwaj@gmail.com.
  • Bryan Yoo
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Banafsheh Salehi
    Department of Radiology, University of California, Los Angeles, United States.
  • Mark Attiah
    Department of Neurosurgery, University of California, Los Angeles, United States.
  • Diane Villaroman
    Department of Psychiatry and Biobehavioral Sciences, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, California, USA.
  • Christine Ahn
    Department of Neurosurgery, University of California, Los Angeles, United States.
  • Matthew Edwards
    Department of Neurosurgery, University of California, Los Angeles, United States.
  • Azim Laiwalla
    Department of Neurosurgery, University of California, Los Angeles, United States.
  • Anshul Ratnaparkhi
    Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles.
  • Ien Li
    David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
  • Kirstin Cook
    David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
  • Joel Beckett
    Department of Neurosurgery, David Geffen School of Medicine, University of California Los Angeles; David Geffen School of Medicine, University of California Los Angeles. Electronic address: JBeckett@mednet.ucla.edu.
  • Luke Macyszyn