Next Article in Journal
Post-Developmental Roles of Notch Signaling in the Nervous System
Next Article in Special Issue
Nerve Segmentation with Deep Learning from Label-Free Endoscopic Images Obtained Using Coherent Anti-Stokes Raman Scattering
Previous Article in Journal
Unveiling the Mechanism of Action of 7α-acetoxy-6β-hydroxyroyleanone on an MRSA/VISA Strain: Membrane and Cell Wall Interactions
Previous Article in Special Issue
Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks
Open AccessArticle

Deep Neural Networks for Dental Implant System Classification

1
Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan
2
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama 700-8558, Japan
3
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193, Japan
4
Polytechnic Center Kagawa, 2-4-3, Hananomiya-cho, Takamatsu, Kagawa 761-8063, Japan
5
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; https://doi.org/10.3390/biom10070984
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
Show Figures

Figure 1

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.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop