Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images
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 im...
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doaj-b53532cc20b04af3a072d8d38c1bd5f92021-06-01T01:41:39ZengMDPI AGBiomolecules2218-273X2021-05-011181581510.3390/biom11060815Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph ImagesShintaro Sukegawa0Kazumasa Yoshii1Takeshi Hara2Tamamo Matsuyama3Katsusuke Yamashita4Keisuke Nakano5Kiyofumi Takabatake6Hotaka Kawai7Hitoshi Nagatsuka8Yoshihiko Furuki9Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, JapanElectronic and Computer Engineering, Department of Electrical, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193, JapanElectronic and Computer Engineering, Department of Electrical, Faculty of Engineering, Gifu University, 1-1 Yanagido, Gifu, Gifu 501-1193, JapanDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, JapanPolytechnic Center Kagawa, 2-4-3, Hananomiya-cho, Takamatsu, Kagawa 761-8063, JapanDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Okayama 700-8558, JapanDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Okayama 700-8558, JapanDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Okayama 700-8558, JapanDentistry and Pharmaceutical Sciences, Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Okayama 700-8558, JapanDepartment of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital, 1-2-1, Asahi-machi, Takamatsu, Kagawa 760-8557, JapanIt 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.https://www.mdpi.com/2218-273X/11/6/815multi-task learningdeep learningartificial intelligencedental implantclassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Tamamo Matsuyama Katsusuke Yamashita Keisuke Nakano Kiyofumi Takabatake Hotaka Kawai Hitoshi Nagatsuka Yoshihiko Furuki |
spellingShingle |
Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Tamamo Matsuyama Katsusuke Yamashita Keisuke Nakano Kiyofumi Takabatake Hotaka Kawai Hitoshi Nagatsuka Yoshihiko Furuki Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images Biomolecules multi-task learning deep learning artificial intelligence dental implant classification |
author_facet |
Shintaro Sukegawa Kazumasa Yoshii Takeshi Hara Tamamo Matsuyama Katsusuke Yamashita Keisuke Nakano Kiyofumi Takabatake Hotaka Kawai Hitoshi Nagatsuka Yoshihiko Furuki |
author_sort |
Shintaro Sukegawa |
title |
Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images |
title_short |
Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images |
title_full |
Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images |
title_fullStr |
Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images |
title_full_unstemmed |
Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images |
title_sort |
multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images |
publisher |
MDPI AG |
series |
Biomolecules |
issn |
2218-273X |
publishDate |
2021-05-01 |
description |
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. |
topic |
multi-task learning deep learning artificial intelligence dental implant classification |
url |
https://www.mdpi.com/2218-273X/11/6/815 |
work_keys_str_mv |
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