Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study.

Journal: Journal of dentistry
PMID:

Abstract

OBJECTIVES: To train and validate a cloud-based convolutional neural network (CNN) model for automated segmentation (AS) of dental implant and attached prosthetic crown on cone-beam computed tomography (CBCT) images.

Authors

  • Bahaaeldeen M Elgarba
    OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Department of Prosthodontics, Faculty of Dentistry, Tanta University, 31511 Tanta, Egypt.
  • Stijn Van Aelst
    OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium.
  • Abdullah Swaity
    OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals Leuven, Belgium, 3000 Leuven, Belgium; Prosthodontic Department, King Hussein Medical Center, Royal Medical Services, Amman, Jordan.
  • Nermin Morgan
    OMFS IMPATH Research Group, Department of Imaging & Pathology, Faculty of Medicine, KU Leuven & Oral and Maxillofacial Surgery, University Hospitals Leuven, Leuven, Belgium.
  • 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.