Deep Learning Prediction of Cervical Spine Surgery Revision Outcomes Using Standard Laboratory and Operative Variables.

Journal: World neurosurgery
PMID: 38408699

Abstract

BACKGROUND: Cervical spine procedures represent a major proportion of all spine surgery. Mitigating the revision rate following cervical procedures requires careful patient selection. While complication risk has successfully been predicted, revision risk has proven more challenging. This is likely due to the absence of granular variables in claims databases. The objective of this study was to develop a state-of-the-art model of revision prediction of cervical spine surgery using laboratory and operative variables.

Authors

  • Ethan Schonfeld
    Neurosurgery AI Lab & Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA.
  • Aaryan Shah
    Neurosurgery Artificial Intelligence Lab, Stanford University School of Medicine, Stanford, California, USA.
  • Thomas Michael Johnstone
    Neurosurgery Artificial Intelligence Lab, Stanford University School of Medicine, Stanford, California, USA.
  • Adrian Rodrigues
    Neurosurgery Artificial Intelligence Lab, Stanford University School of Medicine, Stanford, California, USA.
  • Garret K Morris
    Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, USA.
  • Martin N Stienen
    Department of Neurosurgery, University Hospital Zurich, Zurich, Switzerland.
  • Anand Veeravagu
    Department of Neurosurgery, Stanford Medical Center, Stanford, California.

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