An artificial intelligence proposal to automatic teeth detection and numbering in dental bite-wing radiographs.

Journal: Acta odontologica Scandinavica
PMID: 33176533

Abstract

OBJECTIVES: Radiological examination has an important place in dental practice, and it is frequently used in intraoral imaging. The correct numbering of teeth on radiographs is a routine practice that takes time for the dentist. This study aimed to propose an automatic detection system for the numbering of teeth in bitewing images using a faster Region-based Convolutional Neural Networks (R-CNN) method.

Authors

  • Yasin Yasa
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Ordu University, Ordu, 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.
  • Adem Pekince
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Karabuk University, Karabuk, Turkey.
  • Kaan Orhan
    Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Dentomaxillofacial Radiologist, Ankara University, Ankara, Turkey.
  • Serdar Akarsu
    Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey.
  • Samet Atasoy
    Department of Restorative Dentistry, Faculty of Dentistry, Ordu University, Ordu, Turkey.
  • Elif Bilgir
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskisehir, Turkey.
  • Alper Odabaş
    Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey.
  • Ahmet Faruk Aslan
    Department of Mathematics and Computer Science, Faculty of Science, Eskisehir Osmangazi University, Eskisehir, Turkey.

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