Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study.

Journal: Oral surgery, oral medicine, oral pathology and oral radiology
PMID: 38632035

Abstract

OBJECTIVE: The aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section.

Authors

  • Ali Altındağ
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey. Electronic address: alialtindag1412@gmail.com.
  • Serkan Bahrilli
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Necmettin Erbakan University, Konya, Turkey.
  • Özer Çelik
    Department of Mathematics and Computer, Faculty of Science and Letters, Eskişehir Osmangazi University, Eskişehir, Turkey.
  • İbrahim Şevki Bayrakdar
    Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey.
  • Kaan Orhan
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Dentomaxillofacial Radiologist, Ankara University, Ankara, Turkey.

Keywords

External Resources

Popular Topics
Recent Journals