ID | 63132 |
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Sukegawa, Shintaro
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Matsuyama, Tamamo
Department of Molecular Oral Medicine and Maxillofacial Surgery, Graduate School of Biomedical and Health Sciences, Hiroshima University
Tanaka, Futa
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Hara, Takeshi
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Yoshii, Kazumasa
Department of Intelligence Science and Engineering, Graduate School of Natural Science and Technology, Gifu University
Yamashita, Katsusuke
Polytechnic Center Kagawa
Nakano, Keisuke
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Takabatake, Kiyofumi
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Kawai, Hotaka
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
Nagatsuka, Hitoshi
Department of Oral Pathology and Medicine, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences
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Furuki, Yoshihiko
Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital
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Abstract | Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter's classifications for specific respective tasks.
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Published Date | 2022-01-13
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Publication Title |
Scientific Reports
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Volume | volume12
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Issue | issue1
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Publisher | Nature Portfolio
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Start Page | 684
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ISSN | 2045-2322
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Content Type |
Journal Article
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language |
English
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OAI-PMH Set |
岡山大学
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Copyright Holders | © The Author(s) 2022
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File Version | publisher
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Related Url | isVersionOf https://doi.org/10.1038/s41598-021-04603-y
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License | http://creativecommons.org/licenses/by/4.0/.
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Citation | Sukegawa, S., Matsuyama, T., Tanaka, F. et al. Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars. Sci Rep 12, 684 (2022). https://doi.org/10.1038/s41598-021-04603-y
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