Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs.

Journal: Dento maxillo facial radiology
Published Date: Jan 1, 2021

Abstract

OBJECTIVE: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification of maxillary sinus lesions compared with healthy maxillary sinuses.

Authors

  • Ryosuke Kuwana
    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.
  • Michihito Nozawa
    Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
  • Chiaki Kuwada
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
  • Chisako Muramatsu
    Department of Intelligent Image Information, Graduate School of Medicine, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1194, Japan. Electronic address: chisa@fjt.info.gifu-u.ac.jp.
  • 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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