Assessment of automatic cephalometric landmark identification using artificial intelligence.

Journal: Orthodontics & craniofacial research
PMID: 34842346

Abstract

OBJECTIVE: To compare the accuracy of cephalometric landmark identification between artificial intelligence (AI) deep learning convolutional neural networks (CNN) You Only Look Once, Version 3 (YOLOv3) algorithm and the manually traced (MT) group.

Authors

  • Galina Bulatova
    Department of Orthodontics, College of Dentistry, University of Illinois, Chicago, Illinois, USA.
  • Budi Kusnoto
  • Viana Grace
    Department of Orthodontics, College of Dentistry, University of Illinois, Chicago, Illinois, USA.
  • T Peter Tsay
    Department of Orthodontics, College of Dentistry, University of Illinois, Chicago, Illinois, USA.
  • David M Avenetti
    Department of Pediatric Dentistry, University of Illinois, Chicago, Illinois, USA.
  • Flavio Jose Castelli Sanchez
    Department of Orthodontics, College of Dentistry, University of Illinois, Chicago, Illinois, USA.

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