Automated Cone Beam Computed Tomography Segmentation of Multiple Impacted Teeth With or Without Association to Rare Diseases: Evaluation of Four Deep Learning-Based Methods.

Journal: Orthodontics & craniofacial research
Published Date: Jan 2, 2025

Abstract

OBJECTIVE: To assess the accuracy of three commercially available and one open-source deep learning (DL) solutions for automatic tooth segmentation in cone beam computed tomography (CBCT) images of patients with multiple dental impactions.

Authors

  • Eloi Sinard
    UFR Odontologie, Université Paris Cité, Paris, France.
  • Laurent Gajny
    Arts et Métiers, Institut de Biomécanique Humaine Georges Charpak, 151 bd de l'Hôpital, 75013, Paris, France. laurent.gajny@ensam.eu.
  • Muriel de La Dure-Molla
    UFR Odontologie, Université Paris Cité, Paris, France.
  • Rufino Felizardo
    UFR Odontologie, Université Paris Cité, Paris, France.
  • Gauthier Dot
    UFR odontologie, université Paris Cité, Paris, France - AP-HP, hôpital Pitié-Salpêtrière, service de médecine bucco-dentaire, Paris, France - Institut de biomécanique humaine Georges Charpak, école nationale supérieure d'Arts et Métiers, Paris, France.

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