start-ver=1.4
cd-journal=joma
no-vol=17
cd-vols=
no-issue=7
article-no=
start-page=e0269016
end-page=
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2022
dt-pub=20220727
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?
en-subtitle=
kn-subtitle=
en-abstract=
kn-abstract=Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to "Huge" for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models.
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=TanakaFuta
en-aut-sei=Tanaka
en-aut-mei=Futa
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
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=5
ORCID=

en-aut-name=KagayaTutaro
en-aut-sei=Kagaya
en-aut-mei=Tutaro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
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=7
ORCID=

en-aut-name=TakabatakeKiyofumi
en-aut-sei=Takabatake
en-aut-mei=Kiyofumi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=8
ORCID=

en-aut-name=KawaiHotaka
en-aut-sei=Kawai
en-aut-mei=Hotaka
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=9
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=10
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=11
ORCID=

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

affil-num=2
en-affil=Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, 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=Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
kn-affil=

affil-num=5
en-affil=Polytechnic Center Kagawa
kn-affil=

affil-num=6
en-affil=Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University
kn-affil=

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

affil-num=8
en-affil=Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=9
en-affil=Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=10
en-affil=Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
kn-affil=

affil-num=11
en-affil=Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital
kn-affil=
END