Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.

Journal: Journal of neurosurgery. Spine
PMID: 38552236

Abstract

OBJECTIVE: Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited to some degree by manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry.

Authors

  • Edward S Harake
    1School of Medicine and Departments of.
  • Joseph R Linzey
    Departments of2Neurosurgery and.
  • Cheng Jiang
    Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
  • Rushikesh S Joshi
    Department of Neurological Surgery, University of California, San Francisco, 400 Parnassus Avenue, A850, San Francisco, CA, 94143, USA.
  • Mark M Zaki
    Computational Neurosciences Outcomes Center, Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
  • Jaes C Jones
    2Neurosurgery.
  • Siri Sahib S Khalsa
    Department of Neurosurgery, University of Michigan, Ann Arbor, MI, USA.
  • John H Lee
    1University of Michigan Medical School, Ann Arbor, Michigan.
  • Zachary Wilseck
    5Radiology, University of Michigan, Ann Arbor, Michigan.
  • Jacob R Joseph
    Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan.
  • Todd C Hollon
    Departments of1Neurosurgery.
  • Paul Park
    Department of Neurosurgery, University of Michigan, Ann Arbor, Michigan.

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