A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography.

Journal: Dento maxillo facial radiology
PMID: 30379570

Abstract

OBJECTIVES:: The distal root of the mandibular first molar occasionally has an extra root, which can directly affect the outcome of endodontic therapy. In this study, we examined the diagnostic performance of a deep learning system for classification of the root morphology of mandibular first molars on panoramic radiographs. Dental cone-beam CT (CBCT) was used as the gold standard.

Authors

  • Teruhiko Hiraiwa
    1 Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry , Nagoya , Japan.
  • Yoshiko Ariji
    Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
  • Motoki Fukuda
    Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
  • Yoshitaka Kise
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
  • Kazuhiko Nakata
    2 Department of Endodontics, Aichi-Gakuin University School of Dentistry , Nagoya , Japan.
  • Akitoshi Katsumata
  • Hiroshi Fujita
    Department of Intelligent Image Information, Division of Regeneration and Advanced Medical Sciences, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Eiichiro Ariji
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.

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