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ID 60283
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Sukegawa, Shintaro Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences ORCID Kaken ID publons
Yoshii, Kazumasa Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Hara, Takeshi Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Yamashita, Katsusuke Polytechnic Center Kagawa
Nakano, Keisuke Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences ORCID Kaken ID publons researchmap
Yamamoto, Norio Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital
Nagatsuka, Hitoshi Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences Kaken ID publons researchmap
Furuki, Yoshihiko
Abstract
In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.
Keywords
dental implant
artificial intelligence
classification
deep learning
convolutional neural networks
Published Date
2020-07-01
Publication Title
Biomolecules
Volume
volume10
Issue
issue7
Publisher
MDPI
Start Page
984
ISSN
2218-273X
Content Type
Journal Article
language
英語
OAI-PMH Set
岡山大学
Copyright Holders
© 2020 by the authors.
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publisher
PubMed ID
DOI
Web of Science KeyUT
Related Url
isVersionOf https://doi.org/10.3390/biom10070984
License
http://creativecommons.org/licenses/by/4.0/
Funder Name
Japan Society for the Promotion of Science
助成番号
19K19158