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Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm

Authors
 Jae-Hong Lee  ;  Do-Hyung Kim  ;  Seong-Nyum Jeong  ;  Seong-Ho Choi 
Citation
 Journal of Periodontal & Implant Science, Vol.48(2) : 114-123, 2018 
Journal Title
 Journal of Periodontal & Implant Science 
ISSN
 2093-2278 
Issue Date
2018
Keywords
Artificial intelligence ; Machine learning ; Periodontal diseases ; Supervised machine learning
Abstract
Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/162328
Files in This Item:
T201801392.pdf Download
DOI
10.5051/jpis.2018.48.2.114
Appears in Collections:
1. Journal Papers (연구논문) > 2. College of Dentistry (치과대학) > Dept. of Periodontics (치주과학교실)
Yonsei Authors
최성호(Choi, Seong Ho) ORCID logo https://orcid.org/0000-0001-6704-6124
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