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ID 61020
フルテキストURL
fulltext.pdf 2.07 MB
著者
Yamamoto, Norio Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital
Sukegawa, Shintaro Department of Oral Pathology and Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University ORCID Kaken ID publons
Kitamura, Akira Search Space Inc.
Goto, Ryosuke Search Space Inc.
Noda, Tomoyuki Department of Musculoskeletal Traumatology, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences ORCID Kaken ID publons
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
Kawasaki, Keisuke Department of Orthopaedic Surgery, Kagawa Prefectural Central Hospital
Furuki, Yoshihiko Department of Oral and Maxillofacial Surgery, Kagawa Prefectural Central Hospital
Ozaki, Toshifumi Department of Orthopaedic Surgery, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences Kaken ID publons researchmap
抄録
This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records.
キーワード
osteoporosis
deep learning
hip radiograph
ensemble model
発行日
2020-11-10
出版物タイトル
Biomolecules
10巻
11号
出版者
MDPI
開始ページ
1534
ISSN
2218-273X
資料タイプ
学術雑誌論文
言語
English
OAI-PMH Set
岡山大学
著作権者
© 2020 by the authors.
論文のバージョン
publisher
PubMed ID
DOI
Web of Science KeyUT
関連URL
isVersionOf https://doi.org/10.3390/biom10111534
ライセンス
http://creativecommons.org/licenses/by/4.0/
助成機関名
日本学術振興会
助成番号
JP19K19158
JP19K19159