Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph.

Journal: Journal of dental sciences
Published Date: Oct 12, 2023

Abstract

BACKGROUND/PURPOSE: The preciseness of detecting periodontal bone loss is examiners dependent, and this leads to low reliability. The need for automated assistance systems on dental radiographic images has been increased. To the best of our knowledge, no studies have quantitatively and automatically staged periodontitis using dental periapical radiographs. The purpose of this study was to evaluate periodontal bone loss and periodontitis stage on dental periapical radiographs using deep convolutional neural networks (CNNs).

Authors

  • I-Hui Chen
    Division of Periodontology, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
  • Chia-Hua Lin
    Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
  • Min-Kang Lee
    Division of Family Dentistry, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
  • Tsung-En Chen
    Department of Dentistry, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan.
  • Ting-Hsun Lan
    Division of Prosthodontics, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.
  • Chia-Ming Chang
    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
  • Tsai-Yu Tseng
    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
  • Tsaipei Wang
    Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
  • Je-Kang Du
    Division of Prosthodontics, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan.

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