Using Artificial Intelligence to Identify Three Presenting Phenotypes of Chiari Type-1 Malformation and Syringomyelia.

Journal: Neurosurgery
Published Date: Jun 1, 2025

Abstract

BACKGROUND AND OBJECTIVES: Chiari type-1 malformation (CM1) and syringomyelia (SM) are common related pediatric neurosurgical conditions with heterogeneous clinical and radiological presentations that offer challenges related to diagnosis and management. Artificial intelligence (AI) techniques have been used in other fields of medicine to identify different phenotypic clusters that guide clinical care. In this study, we use a novel, combined data-driven and clinician input feature selection process and AI clustering to differentiate presenting phenotypes of CM1 + SM.

Authors

  • Vivek Prakash Gupta
    Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis , Missouri , USA.
  • Ziqi Xu
    Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis , Missouri , USA.
  • Jacob K Greenberg
    Department of Neurosurgery, 660 S Euclid Ave., Box 8057, St. Louis, MO 63110, USA. Electronic address: greenbj3@ccf.org.
  • Jennifer Mae Strahle
    Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis , Missouri , USA.
  • Gabriel Haller
    Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis , Missouri , USA.
  • Thanda Meehan
    Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis , Missouri , USA.
  • Ashley Roberts
    Department of Neurosurgery, Washington University School of Medicine in St. Louis, St. Louis , Missouri , USA.
  • David D Limbrick
    Department of Pediatrics, Washington University School of Medicine, St. Louis, 63110, USA.
  • Chenyang Lu
    Department of Computer Science and Engineering, Washington University, St. Louis, MO.

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