Automated classification of skeletal malocclusion in German orthodontic patients.

Journal: Clinical oral investigations
Published Date:

Abstract

OBJECTIVES: Precisely diagnosing skeletal class is mandatory for correct orthodontic treatment. Artificial intelligence (AI) could increase efficiency during diagnostics and contribute to automated workflows. So far, no AI-driven process can differentiate between skeletal classes I, II, and III in German orthodontic patients. This prospective cross-sectional study aimed to develop machine- and deep-learning models for diagnosing their skeletal class based on the gold-standard individualised ANB of Panagiotidis and Witt.

Authors

  • Eva Paddenberg-Schubert
    Department of Orthodontics, University Hospital of Regensburg, University of Regensburg, 93047, Regensburg, Germany.
  • Kareem Midlej
    Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, University of Tel-Aviv, Tel-Aviv, 6997801, Israel.
  • Sebastian Krohn
    Department of Orthodontics, University Hospital of Regensburg, University of Regensburg, 93047, Regensburg, Germany.
  • Erika Kuchler
    Department of Orthodontics, University of Bonn, D-53111, Bonn, Germany.
  • Nezar Watted
    Center for Dentistry Research and Aesthetics, Jatt, 4491800, Israel.
  • Peter Proff
    Department of Orthodontics, University Hospital of Regensburg, University of Regensburg, 93047, Regensburg, Germany.
  • Fuad A Iraqi
    Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, University of Tel-Aviv, Tel-Aviv, 6997801, Israel. fuadi@tauex.tau.ac.il.