Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction.

Journal: Orthodontics & craniofacial research
Published Date: Dec 1, 2021

Abstract

OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers.

Authors

  • Lily Etemad
    Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA.
  • Tai-Hsien Wu
  • Parker Heiner
    College of Dentistry, The Ohio State University, Columbus, OH, USA.
  • Jie Liu
    School of Bioscience and Bioengineering, South China University of Technology, Guangzhou, China.
  • Sanghee Lee
    Division of Orthodontics, College of Dentistry, The Ohio State University, Columbus, OH, USA.
  • Wei-Lun Chao
    Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio, USA.
  • Mary Lanier Zaytoun
    Private Dental Practice, Raleigh, NC, USA.
  • Camille Guez
    Private Dental Practice, Carpentras, France.
  • Feng-Chang Lin
  • Christina Bonebreak Jackson
    SOVE Inc., Chapel Hill, NC, USA.
  • Ching-Chang Ko

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