Use of machine learning to model surgical decision-making in lumbar spine surgery.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Published Date: Jan 28, 2022

Abstract

PURPOSE: The majority of lumbar spine surgery referrals do not proceed to surgery. Early identification of surgical candidates in the referral process could expedite their care, whilst allowing timelier implementation of non-operative strategies for those who are unlikely to require surgery. By identifying clinical and imaging features associated with progression to surgery in the literature, we aimed to develop a machine learning model able to mirror surgical decision-making and calculate the chance of surgery based on the identified features.

Authors

  • Nathan Xie
    School of Medical Sciences, University of New South Wales, Prince of Wales Private Hospital, High Street, Kensington, Sydney, 2052, Australia. nathanxie9@gmail.com.
  • Peter J Wilson
    School of Medical Sciences, University of New South Wales, Prince of Wales Private Hospital, High Street, Kensington, Sydney, 2052, Australia.
  • Rajesh Reddy
    School of Medical Sciences, University of New South Wales, Prince of Wales Private Hospital, High Street, Kensington, Sydney, 2052, Australia.

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