Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

Journal: European journal of orthodontics
PMID: 38895901

Abstract

OBJECTIVES: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images.

Authors

  • Julie Hendrickx
    Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
  • Rellyca Sola Gracea
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
  • Michiel Vanheers
    Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
  • Nicolas Winderickx
    Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, 3000 Leuven, Belgium.
  • Flavia Preda
    OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Kapucijnenvoer33, BE-3000 Leuven, Belgium. Electronic address: flaviacpreda@gmail.com.
  • Sohaib Shujaat
    OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, University of Leuven and Oral & Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
  • Reinhilde Jacobs
    OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, University of Leuven and Department of Oral & Maxillofacial Surgery, University Hospitals Leuven, KU Leuven, Kapucijnenvoer 7, 3000, Leuven, Belgium; Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden. Electronic address: reinhilde.jacobs@ki.se.

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