start-ver=1.4
cd-journal=joma
no-vol=10
cd-vols=
no-issue=7
article-no=
start-page=984
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2020
dt-pub=20200701
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Deep Neural Networks for Dental Implant System Classification
en-subtitle=
kn-subtitle=
en-abstract=
kn-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.
en-copyright=
kn-copyright=

en-aut-name=SukegawaShintaro 
en-aut-sei=Sukegawa
en-aut-mei=Shintaro 
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=YoshiiKazumasa
en-aut-sei=Yoshii
en-aut-mei=Kazumasa
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=HaraTakeshi
en-aut-sei=Hara
en-aut-mei=Takeshi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=YamashitaKatsusuke
en-aut-sei=Yamashita
en-aut-mei=Katsusuke
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=NakanoKeisuke
en-aut-sei=Nakano
en-aut-mei=Keisuke
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=YamamotoNorio
en-aut-sei=Yamamoto
en-aut-mei=Norio
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

en-aut-name=NagatsukaHitoshi
en-aut-sei=Nagatsuka
en-aut-mei=Hitoshi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=7
ORCID=

en-aut-name=FurukiYoshihiko
en-aut-sei=Furuki
en-aut-mei=Yoshihiko
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=8
ORCID=

affil-num=1
en-affil=Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
kn-affil=

affil-num=2
en-affil=Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
kn-affil=

affil-num=3
en-affil=Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
kn-affil=

affil-num=4
en-affil=Polytechnic Center Kagawa
kn-affil=

affil-num=5
en-affil=Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
kn-affil=

affil-num=6
en-affil=Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital
kn-affil=

affil-num=7
en-affil=Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
kn-affil=

affil-num=8
en-affil=
kn-affil=
en-keyword=dental implant
kn-keyword=dental implant
en-keyword=artificial intelligence
kn-keyword=artificial intelligence
en-keyword=classification
kn-keyword=classification
en-keyword=deep learning
kn-keyword=deep learning
en-keyword=convolutional neural networks
kn-keyword=convolutional neural networks
END