Artificial intelligence for segmentation and classification in lumbar spinal stenosis: an overview of current methods.

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
PMID: 39883162

Abstract

PURPOSE: Lumbar spinal stenosis (LSS) is a frequently occurring condition defined by narrowing of the spinal or nerve root canal due to degenerative changes. Physicians use MRI scans to determine the severity of stenosis, occasionally complementing it with X-ray or CT scans during the diagnostic work-up. However, manual grading of stenosis is time-consuming and induces inter-reader variability as a standardized grading system is lacking. Machine Learning (ML) has the potential to aid physicians in this process by automating segmentation and classification of LSS. However, it is unclear what models currently exist to perform these tasks.

Authors

  • E J A Verheijen
    Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA. e.j.a.verheijen@lumc.nl.
  • T Kapogiannis
    Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
  • D Munteh
    Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
  • J Chabros
    Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
  • M Staring
    Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
  • T R Smith
    Computational Neuroscience Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
  • C L A Vleggeert-Lankamp
    Department of Neurosurgery, Leiden University Medical Center, Albinusdreef 2, 2333ZA, Leiden, The Netherlands.

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