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Deep Neural Networks for Dental Implant System Classification

Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193, Japan
Polytechnic Center Kagawa, 2-4-3, Hananomiya-cho, Takamatsu, Kagawa 761-8063, Japan
Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital, Takamatsu, Kagawa 760-8557, Japan
Author to whom correspondence should be addressed.
Biomolecules 2020, 10(7), 984;
Received: 30 May 2020 / Revised: 27 June 2020 / Accepted: 29 June 2020 / Published: 1 July 2020
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research)
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. View Full-Text
Keywords: dental implant; artificial intelligence; classification; deep learning; convolutional neural networks dental implant; artificial intelligence; classification; deep learning; convolutional neural networks
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MDPI and ACS Style

Sukegawa, S.; Yoshii, K.; Hara, T.; Yamashita, K.; Nakano, K.; Yamamoto, N.; Nagatsuka, H.; Furuki, Y. Deep Neural Networks for Dental Implant System Classification. Biomolecules 2020, 10, 984.

AMA Style

Sukegawa S, Yoshii K, Hara T, Yamashita K, Nakano K, Yamamoto N, Nagatsuka H, Furuki Y. Deep Neural Networks for Dental Implant System Classification. Biomolecules. 2020; 10(7):984.

Chicago/Turabian Style

Sukegawa, Shintaro; Yoshii, Kazumasa; Hara, Takeshi; Yamashita, Katsusuke; Nakano, Keisuke; Yamamoto, Norio; Nagatsuka, Hitoshi; Furuki, Yoshihiko. 2020. "Deep Neural Networks for Dental Implant System Classification" Biomolecules 10, no. 7: 984.

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