Evaluation of an artificial intelligence-based algorithm for automated localization of craniofacial landmarks.

Journal: Clinical oral investigations
Published Date: Apr 4, 2023

Abstract

OBJECTIVES: Due to advancing digitalisation, it is of interest to develop standardised and reproducible fully automated analysis methods of cranial structures in order to reduce the workload in diagnosis and treatment planning and to generate objectifiable data. The aim of this study was to train and evaluate an algorithm based on deep learning methods for fully automated detection of craniofacial landmarks in cone-beam computed tomography (CBCT) in terms of accuracy, speed, and reproducibility.

Authors

  • Friederike Maria Sophie Blum
    Department of Orthodontics, University Hospital of RWTH Aachen, Pauwelsstraße 30, D-52074, Aachen, Germany. frblum@ukaachen.de.
  • Stephan Christian Möhlhenrich
    Department of Orthodontics, Witten/Herdecke University, Witten, Germany.
  • Stefan Raith
    Department of Dental Materials and Biomaterials Research, RWTH Aachen University Hospital, Aachen, Germany. Electronic address: sraith@ukaachen.de.
  • Tobias Pankert
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany. tpankert@ukaachen.de.
  • Florian Peters
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany.
  • Michael Wolf
    Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
  • Frank Hölzle
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany.
  • Ali Modabber
    Department of Oral and Maxillofacial Surgery, RWTH Aachen University Hospital, Aachen, Germany.

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