Comparison of deep learning models to detect crossbites on 2D intraoral photographs.

Journal: Head & face medicine
PMID: 39223562

Abstract

BACKGROUND: To support dentists with limited experience, this study trained and compared six convolutional neural networks to detect crossbites and classify non-crossbite, frontal, and lateral crossbites using 2D intraoral photographs.

Authors

  • Beatrice Noeldeke
    Leibniz University of Hanover, Königsworther Platz 1, 30167, Hanover, Germany.
  • Stratos Vassis
  • Mohammedreza Sefidroodi
    Section of Orthodontics, Department of Dentistry and Oral Health, Aarhus University, Vennelyst Boulevard 9, Aarhus C, Aarhus, 8000, Denmark.
  • Ruben Pauwels
    Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Høegh-Guldbergs Gade 6B, 8000-C, Aarhus, Denmark. pauwelsruben@hotmail.com.
  • Peter Stoustrup
    Section of Orthodontics, Department of Dentistry and Oral Health, Aarhus University, Vennelyst Boulevard 9, Aarhus C, Aarhus, 8000, Denmark.

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