Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.

Journal: Dento maxillo facial radiology
PMID: 38183164

Abstract

OBJECTIVES: Improved tools based on deep learning can be used to accurately number and identify teeth. This study aims to review the use of deep learning in tooth numbering and identification.

Authors

  • Soroush Sadr
    Department of Endodontics, School of Dentistry, Hamadan University of Medical Sciences, Hamadan, Iran.
  • Rata Rokhshad
    Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
  • Yasaman Daghighi
    School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran 1983963113, Iran.
  • Mohsen Golkar
    Department of Oral and Maxillofacial Surgery, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran 4188794755, Iran.
  • Fateme Tolooie Kheybari
    Faculty of Dentistry, Tabriz Medical Sciences, Islamic Azad University, Tabriz 5166/15731, Iran.
  • Fatemeh Gorjinejad
    Faculty of Dentistry, Dental School of Islamic Azad University of Medical Sciences, Tehran 19395/1495, Iran.
  • Atousa Mataji Kojori
    Faculty of Dentistry, Dental School of Islamic Azad University of Medical Sciences, Tehran 19395/1495, Iran.
  • Parisa Rahimirad
    Student Research Committee, School of Dentistry, Guilan University of Medical Sciences, Rasht 4188794755, Iran.
  • Parnian Shobeiri
    School of Medicine, Tehran University of Medical Science, Tehran, Iran.
  • Mina Mahdian
    Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA.
  • Hossein Mohammad-Rahimi
    Division of Artificial Intelligence Imaging Research, University of Maryland School of Dentistry, Baltimore, MD 21201, USA.

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