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Biomolecules
Volume 11
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10.3390/biom11060815
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Article

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

by
Shintaro Sukegawa
1,2,*,
Kazumasa Yoshii
3,
Takeshi Hara
3,4,
Tamamo Matsuyama
1,
Katsusuke Yamashita
5,
Keisuke Nakano
2,
Kiyofumi Takabatake
2,
Hotaka Kawai
2,
Hitoshi Nagatsuka
2 and
Yoshihiko Furuki
1
1
Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, Japan
2
Dentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Okayama 700-8558, Japan
3
Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193, Japan
4
Center for Healthcare Information Technology, Tokai National Higher Education and Research System, 1-1 Yanagido, Gifu, Gifu 501-1193, Japan
5
Polytechnic Center Kagawa, 2-4-3, Hananomiya-cho, Takamatsu, Kagawa 761-8063, Japan
*
Author to whom correspondence should be addressed.
Biomolecules 2021, 11(6), 815; https://doi.org/10.3390/biom11060815
Submission received: 21 April 2021 / Revised: 19 May 2021 / Accepted: 26 May 2021 / Published: 30 May 2021
(This article belongs to the Special Issue Application of Artificial Intelligence for Medical Research, 2nd Edition)
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Abstract

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.
Keywords: multi-task learning; deep learning; artificial intelligence; dental implant; classification multi-task learning; deep learning; artificial intelligence; dental implant; classification

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MDPI and ACS Style

Sukegawa, S.; Yoshii, K.; Hara, T.; Matsuyama, T.; Yamashita, K.; Nakano, K.; Takabatake, K.; Kawai, H.; Nagatsuka, H.; Furuki, Y. Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images. Biomolecules 2021, 11, 815. https://doi.org/10.3390/biom11060815

AMA Style

Sukegawa S, Yoshii K, Hara T, Matsuyama T, Yamashita K, Nakano K, Takabatake K, Kawai H, Nagatsuka H, Furuki Y. Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images. Biomolecules. 2021; 11(6):815. https://doi.org/10.3390/biom11060815

Chicago/Turabian Style

Sukegawa, Shintaro, Kazumasa Yoshii, Takeshi Hara, Tamamo Matsuyama, Katsusuke Yamashita, Keisuke Nakano, Kiyofumi Takabatake, Hotaka Kawai, Hitoshi Nagatsuka, and Yoshihiko Furuki. 2021. "Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images" Biomolecules 11, no. 6: 815. https://doi.org/10.3390/biom11060815

APA Style

Sukegawa, S., Yoshii, K., Hara, T., Matsuyama, T., Yamashita, K., Nakano, K., Takabatake, K., Kawai, H., Nagatsuka, H., & Furuki, Y. (2021). Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images. Biomolecules, 11(6), 815. https://doi.org/10.3390/biom11060815

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