Machine learning identifies clusters of the normal adolescent spine based on sagittal balance.

Journal: Spine deformity
PMID: 39167356

Abstract

PURPOSE: This study applied a machine learning semi-supervised clustering approach to radiographs of adolescent sagittal spines from a single pediatric institution to identify patterns of sagittal alignment in the normal adolescent spine. We sought to explore the inherent variability found in adolescent sagittal alignment using machine learning to remove bias and determine whether clusters of sagittal alignment exist.

Authors

  • Dion G Birhiray
    Georgetown University School of Medicine, Washington, D.C, USA. dgb61@georgetown.edu.
  • Srikhar V Chilukuri
    Baylor College of Medicine, Houston, TX, USA.
  • Caleb C Witsken
    Baylor College of Medicine, Houston, TX, USA.
  • Maggie Wang
    Baylor College of Medicine, Houston, TX, USA.
  • Jacob P Scioscia
    Baylor College of Medicine, Houston, TX, USA.
  • Martin Gehrchen
    Righospitalet and University of Copenhagen, Copenhagen, Europe, Denmark.
  • Lorenzo R Deveza
    Baylor College of Medicine, Houston, TX, USA.
  • Benny Dahl
    Righospitalet and University of Copenhagen, Copenhagen, Europe, Denmark.

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