Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning.

Journal: Plastic and reconstructive surgery
PMID: 36696326

Abstract

BACKGROUND: Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-dimensional skull shape analysis with an unsupervised machine-learning algorithm to generate a quantitative shape severity score (cranial morphology deviation) and provide an operative threshold score.

Authors

  • Erin E Anstadt
    From the University of Pittsburgh Medical Center, Department of Plastic Surgery.
  • Wenzheng Tao
    School of Computing, University of Utah, Salt Lake City, UT, USA.
  • Ejay Guo
    School of Computing, University of Utah.
  • Lucas Dvoracek
    From the University of Pittsburgh Medical Center, Department of Plastic Surgery.
  • Madeleine K Bruce
    Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  • Philip J Grosse
    Clinical and Translational Science Institute, University of Pittsburgh.
  • Li Wang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Ladislav Kavan
    School of Computing, University of Utah.
  • Ross Whitaker
    School of Computing, University of Utah, Salt Lake City, UT, USA.
  • Jesse A Goldstein
    Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA.

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