https://www.ncbi.nlm.nih.gov/pubmed/30056118
Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.
Author information
- 1
- Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry, Daejeon, Republic of Korea. Electronic address: ljaehong@gmail.com.
- 2
- Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry, Daejeon, Republic of Korea.
- 3
- Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry, Seoul, Republic of Korea.
Abstract
OBJECTIVES:
Deep convolutional neural networks (CNNs) are a rapidly emerging new area of medical research, and have yielded impressive results in diagnosis and prediction in the fields of radiology and pathology. The aim of the current study was to evaluate the efficacy of deep CNN algorithms for detection and diagnosis of dental caries on periapical radiographs.
MATERIALS AND METHODS:
A total of 3000 periapical radiographic images were divided into a training and validation dataset (n = 2400 [80%]) and a test dataset (n = 600 [20%]). A pre-trained GoogLeNet Inception v3 CNN network was used for preprocessing and transfer learning. The diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the curve (AUC) were calculated for detection and diagnostic performance of the deep CNN algorithm.
RESULTS:
The diagnostic accuracies of premolar, molar, and both premolar and molar models were 89.0% (80.4-93.3), 88.0% (79.2-93.1), and 82.0% (75.5-87.1), respectively. The deep CNN algorithm achieved an AUC of 0.917 (95% CI 0.860-0.975) on premolar, an AUC of 0.890 (95% CI 0.819-0.961) on molar, and an AUC of 0.845 (95% CI 0.790-0.901) on both premolar and molar models. The premolar model provided the best AUC, which was significantly greater than those for other models (P < 0.001).
CONCLUSIONS:
This study highlighted the potential utility of deep CNN architecture for the detection and diagnosis of dental caries. A deep CNN algorithm provided considerably good performance in detecting dental caries in periapical radiographs. CLINICAL SIGNIfiCANCE: Deep CNN algorithms are expected to be among the most effective and efficient methods for diagnosing dental caries.
Copyright © 2018 Elsevier Ltd. All rights reserved.
KEYWORDS:
Artificial intelligence; Dental caries; Machine learning; Supervised machine learning
- PMID:
- 30056118
- DOI:
- 10.1016/j.jdent.2018.07.015
원광대, 연세대.
3000 장의 이미지.
pre-trained GoogLeNet Inception v3 CNN 모델을 사용함.
치아우식증을 진단.
정확도 : 82 ~89 % (어금니 위치에 따라 약간 차이)
2018 JoD Detection and diagnosis of dental caries using a deep learning-based CNN.pdf
