YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.

Journal: BMC medical imaging
PMID: 38992601

Abstract

OBJECTIVES: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.

Authors

  • Busra Beser
    Faculty of Dentistry, Department of Orthodontics, Recep Tayyip Erdoğan University, Menderes Boulevard No 612, 53020, Rize, Turkey.
  • Tugba Reis
    Pedodontics, Private Practice, Trabzon, Turkey.
  • Merve Nur Berber
    Department of Orthodontics, Faculty of Dentistry, Recep Tayyip Erdogan University, Rize, Turkey.
  • Edanur Topaloglu
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Inonu University, Malatya, Turkey.
  • Esra Gungor
    Department of Pedodontics, Faculty of Dentistry, Inonu University, Malatya, Turkey.
  • Münevver Coruh Kılıc
    Department of Paediatric Dentistry, Faculty of Dentistry, Ataturk University, Erzurum, Turkey.
  • Sacide Duman
    Department of Paediatric Dentistry, Faculty of Dentistry, Inonu University, Malatya, 44280, Turkey. sacidetuncduman@gmail.com.
  • Özer Çelik
    Department of Mathematics and Computer, Faculty of Science and Letters, Eskişehir Osmangazi University, Eskişehir, Turkey.
  • Alican Kuran
    Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kocaeli University, Kocaeli 41190, Turkey.
  • İbrahim Şevki Bayrakdar
    Department of Oral Diagnosis and Radiology, Faculty of Dentistry, Eskişehir Osmangazi University, Eskişehir, Turkey.

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