Optimal Implant Sizing Using Machine Learning Is Associated With Increased Range of Motion After Cervical Disk Arthroplasty.

Journal: Neurosurgery
PMID: 38551347

Abstract

BACKGROUND AND OBJECTIVES: Cervical disk arthroplasty (CDA) offers the advantage of motion preservation in the treatment of focal cervical pathology. At present, implant sizing is performed using subjective tactile feedback and imaging of trial cages. This study aims to construct interpretable machine learning (IML) models to accurately predict postoperative range of motion (ROM) and identify the optimal implant sizes that maximize ROM in patients undergoing CDA.

Authors

  • Nikita Lakomkin
    1Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.
  • Zach Pennington
    Department of Neurosurgery, The Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Archis Bhandarkar
    Mayo Clinic Neuro-Informatics Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Department of Neurological Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Anthony L Mikula
    1Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.
  • Giorgos D Michalopoulos
    Department of Neurological Surgery, Mayo Clinic, Rochester , Minnesota , USA.
  • Konstantinos Katsos
    Department of Neurological Surgery, Mayo Clinic, Rochester , Minnesota , USA.
  • Selby Chen
    Department of Neurological Surgery, Mayo Clinic, Jacksonville , Florida , USA.
  • Jamal McClendon
    Department of Neurological Surgery, Mayo Clinic, Phoenix , Arizona , USA.
  • Brett A Freedman
    Department of Orthopaedic Surgery, Mayo Clinic, Rochester , Minnesota , USA.
  • Mohamad Bydon
    4Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota.

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