Development of a Machine-Learning Algorithm to Identify Cauda Equina Compression on Magnetic Resonance Imaging Scans.

Journal: World neurosurgery
Published Date: Feb 20, 2025

Abstract

OBJECTIVE: Cauda equina syndrome (CES) poses significant neurological risks if untreated. Diagnosis relies on clinical and radiological features. As the symptoms are often nonspecific and common, the diagnosis is usually made after a magnetic resonance imaging (MRI) scan. A huge number of MRI scans are done to exclude CES but nearly 80% of them will not have CES. This study aimed to develop and validate a machine-learning model for automated CES detection from MRI scans to enable faster triage of patients presenting with CES like clinical features.

Authors

  • Sayan Biswas
    Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL Manchester, England, United Kingdom. Electronic address: sayan.biswas@nca.nhs.uk.
  • Ved Sarkar
    College of Letters and Sciences, University of California, Berkeley, CA 94720, United States of America.
  • Joshua Ian MacArthur
    Department of Surgery and Cancer, Imperial College London, Northwick Park Hospital, London Northwest University Healthcare, Harrow, England, United Kingdom.
  • Li Guo
    Department of Dental Implantology, Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Institute of Stomatology, Nanjing University, Nanjing, China.
  • Xutao Deng
    Department of Electrical Engineering, Edge Hill University, Lancashire, England, United Kingdom.
  • Ella Snowdon
    Faculty of Biology, Medicine and Health, University of Manchester, M13 9PL Manchester, England, United Kingdom.
  • Hamza Ahmed
    Department of Trauma and Orthopaedics, Salford Royal Hospital, Manchester, England, United Kingdom.
  • Callum Tetlow
    Division of Data Science, The Northern Care Alliance NHS Group, M6 8HD Manchester, England, United Kingdom.
  • K Joshi George
    Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal Hospital, Manchester, England, United Kingdom.

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