Deep-learning systems for diagnosing cleft palate on panoramic radiographs in patients with cleft alveolus.

Journal: Oral radiology
PMID: 35984588

Abstract

OBJECTIVES: The aim of the present study was to create effective deep learning-based models for diagnosing the presence or absence of cleft palate (CP) in patients with unilateral or bilateral cleft alveolus (CA) on panoramic radiographs.

Authors

  • Chiaki Kuwada
    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.
  • Yoshitaka Kise
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.
  • Motoki Fukuda
    Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
  • Masako Nishiyama
    Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
  • Takuma Funakoshi
    Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
  • Rihoko Takeuchi
    Department of General Dentistry, Dental Hospital, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, Japan.
  • Airi Sana
    Department of General Dentistry, Dental Hospital, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, Japan.
  • Norinaga Kojima
    Department of General Dentistry, Aichi-Gakuin University School of Dentistry, Dental Hospital, Nagoya, Japan.
  • Eiichiro Ariji
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.

Keywords

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