Deep learning for determining the difficulty of endodontic treatment: a pilot study.

Journal: BMC oral health
PMID: 38760686

Abstract

BACKGROUND: To develop and validate a deep learning model for automated assessment of endodontic case difficulty from periapical radiographs.

Authors

  • Hamed Karkehabadi
    Department of Endodontics, Dental School, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Elham Khoshbin
    Department of Endodontics, Dental School, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Nikoo Ghasemi
    Faculty of Dentistry, Zanjan University of Medical Sciences, Zanjan, Iran.
  • Amal Mahavi
    Department of Endodontics, Dental School, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Hossein Mohammad-Rahimi
    Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.
  • Soroush Sadr
    Department of Endodontics, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran.

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