Machine learning in predicting cauda equina imaging outcomes- a solution to the problem.

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: Dec 3, 2024

Abstract

PURPOSE: Cauda Equina Syndrome (CES) is a rare surgical emergency. The implications for loss of quality of life through delayed management are high, though no clinical symptom is pathognomonic in its diagnosis. We describe how machine learning based algorithms can be used in triaging patients with suspected CES (CES-S).

Authors

  • Rosa Sun
    Department of Neurosurgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK.
  • Abdelmageed Abdelrahman Ramadan
    Swansea Bay University Health Board, Mirrston Hospital, Heol Maes Eglwys, Treforys, Swansea, SA6 6NL, UK.
  • Thaaqib Nazar
    Department of Neurosurgery, University Hospitals Coventry Warwickshire, Clifford Bridge Road, Coventry, CV2 2DX, UK.
  • Ghayur Abbas
    Department of Neurosurgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK.
  • Amin Andalib
    Department of Neurosurgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK.
  • Azam Majeed
    Department of Emergency Medicine, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK.
  • Jasmeet Dhir
    Department of Neurosurgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK.
  • Marcin Czyz
    Department of Neurosurgery, Queen Elizabeth Hospital, Mindelsohn Way, Birmingham, B15 2TH, UK. marcin.czyz@uhb.nhs.uk.

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