Mandibular premolar identification system based on a deep learning model.

Journal: Journal of oral biosciences
Published Date:

Abstract

OBJECTIVES: For constructing an isolated tooth identification system using deep learning, Igarashi et al. (2021) began constructing a learning model as basic research to identify the left and right mandibular first and second premolars. These teeth were chosen for analysis because they are difficult to identify from one another. The learning method itself was proven appropriate but presented low accuracy. Therefore, further improvement in the learning data should increase the accuracy of the model. The study objectives were to modify the learning data and increase the learning model accuracy for enabling the identification of isolated lower premolars.

Authors

  • Yuriko Igarashi
    Department of Anatomy, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho Nishi, Matsudo City, Chiba 271-8587, Japan. Electronic address: igarashi.yuriko@nihon-u.ac.jp.
  • Shintaro Kondo
    Department of Anatomy, Nihon University School of Dentistry at Matsudo, 2-870-1 Sakaecho Nishi, Matsudo City, Chiba 271-8587, Japan.
  • Sora Kida
    Department of Precision Machinery Engineering, Nihon University College of Science and Technology, 7-24-1 Narashinodai, Funabashi City, Chiba 274-8501, Japan.
  • Megumi Aibara
    Department of Precision Machinery Engineering, Nihon University College of Science and Technology, 7-24-1 Narashinodai, Funabashi City, Chiba 274-8501, Japan.
  • Minami Kaneko
    Department of Precision Machinery Engineering, Nihon University College of Science and Technology, 7-24-1 Narashinodai, Funabashi City, Chiba 274-8501, Japan.
  • Fumio Uchikoba
    Department of Precision Machinery Engineering, Nihon University College of Science and Technology, 7-24-1 Narashinodai, Funabashi City, Chiba 274-8501, Japan.