Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images.

Journal: Oral radiology
PMID: 38990220

Abstract

OBJECTIVE: The present study aimed to assess the consistencies and performances of deep learning (DL) models in the diagnosis of condylar osteoarthritis (OA) among patients with dentofacial deformities using panoramic temporomandibular joint (TMJ) projection images.

Authors

  • Yukiko Iwase
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan.
  • Tomoya Sugiki
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, 2-11 Suemori-Dori, Chikusa-Ku, Nagoya, 464-8651, Japan.
  • Yoshitaka Kise
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, 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.
  • Michihito Nozawa
    Department of Oral and Maxillofacial Radiology, Aichi-Gakuin University School of Dentistry, 2-11 Suemori-dori, Chikusa-ku, Nagoya, 464-8651, Japan.
  • Motoki Fukuda
    Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
  • Yoshiko Ariji
    Department of Oral Radiology, School of Dentistry, Osaka Dental University, Osaka, Japan.
  • Eiichiro Ariji
    Department of Oral and Maxillofacial Radiology, Aichi Gakuin University School of Dentistry, Nagoya, Japan.

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