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ID 60283
フルテキストURL
fulltext.pdf 2.09 MB
著者
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
抄録
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.
キーワード
dental implant
artificial intelligence
classification
deep learning
convolutional neural networks
発行日
2020-07-01
出版物タイトル
Biomolecules
10巻
7号
出版者
MDPI
開始ページ
984
ISSN
2218-273X
資料タイプ
学術雑誌論文
言語
English
OAI-PMH Set
岡山大学
著作権者
© 2020 by the authors.
論文のバージョン
publisher
PubMed ID
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.3390/biom10070984
ライセンス
http://creativecommons.org/licenses/by/4.0/
助成機関名
日本学術振興会
助成番号
19K19158