Performance of deep learning models constructed using panoramic radiographs from two hospitals to diagnose fractures of the mandibular condyle.

Journal: Dento maxillo facial radiology
PMID: 33769840

Abstract

OBJECTIVE: The present study aimed to verify the classification performance of deep learning (DL) models for diagnosing fractures of the mandibular condyle on panoramic radiographs using data sets from two hospitals and to compare their internal and external validities.

Authors

  • Masako Nishiyama
    Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
  • Kenichiro Ishibashi
    Department of Maxillofacial Surgery, 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.
  • Wataru Nishiyama
  • Masahiro Umemura
    Department of Oral and Maxillofacial Surgery, Ogaki Municipal Hospital, Ogaki, 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.

Keywords

External Resources

Popular Topics
Recent Journals