TY - JOUR
T1 - Deep neural networks for dental implant system classification
AU - Sukegawa, Shintaro
AU - Yoshii, Kazumasa
AU - Hara, Takeshi
AU - Yamashita, Katsusuke
AU - Nakano, Keisuke
AU - Yamamoto, Norio
AU - Nagatsuka, Hitoshi
AU - Furuki, Yoshihiko
N1 - Funding Information:
Funding: This research was funded by JSPS KAKENHI, grant number JP19K19158.
Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Classification
KW - Convolutional neural networks
KW - Deep learning
KW - Dental implant
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U2 - 10.3390/biom10070984
DO - 10.3390/biom10070984
M3 - Article
C2 - 32630195
AN - SCOPUS:85087385076
VL - 10
SP - 1
EP - 13
JO - Biomolecules
JF - Biomolecules
SN - 2218-273X
IS - 7
M1 - 984
ER -