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- Title
Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach.
- Authors
Ryu, Jihye; Lee, Dong-Min; Jung, Yun-Hoa; Kwon, OhJin; Park, SunYoung; Hwang, JaeJoon; Lee, Jae-Yeol
- Abstract
(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs.
- Subjects
HELSINKI (Finland); DEEP learning; CONVOLUTIONAL neural networks; RADIOGRAPHS; ALVEOLAR process; COMPUTER-aided diagnosis; PERIODONTAL disease
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 9, p5261
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13095261