ID | 64017 |
フルテキストURL | |
著者 |
Sukegawa, Shintaro
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
ORCID
Kaken ID
publons
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 Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University
Yamashita, Katsusuke
Polytechnic Center Kagawa
Nakano, Keisuke
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
ORCID
Kaken ID
publons
researchmap
Takabatake, Kiyofumi
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
Kaken ID
publons
researchmap
Kawai, Hotaka
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
Nagatsuka, Hitoshi
Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University
Kaken ID
publons
researchmap
Furuki, Yoshihiko
Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital
|
抄録 | In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.
|
発行日 | 2022-10-08
|
出版物タイトル |
Scientific Reports
|
巻 | 12巻
|
号 | 1号
|
出版者 | Nature Portfolio
|
開始ページ | 16925
|
ISSN | 2045-2322
|
資料タイプ |
学術雑誌論文
|
言語 |
英語
|
OAI-PMH Set |
岡山大学
|
著作権者 | © The Author(s) 2022
|
論文のバージョン | publisher
|
PubMed ID | |
DOI | |
Web of Science KeyUT | |
関連URL | isVersionOf https://doi.org/10.1038/s41598-022-21408-9
|
ライセンス | http://creativecommons.org/licenses/by/4.0/
|