WEKO3
アイテム
Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning
https://tokushima-u.repo.nii.ac.jp/records/2007954
https://tokushima-u.repo.nii.ac.jp/records/2007954e7d80855-95ea-422e-a7e3-79898e2cc376
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
biom_10_5_665.pdf (2.29 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||||||||||||
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| 公開日 | 2020-09-17 | |||||||||||||||||||||||||||||
| アクセス権 | ||||||||||||||||||||||||||||||
| アクセス権 | open access | |||||||||||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||||||||||||
| 関連識別子 | https://doi.org/10.3390/biom10050665 | |||||||||||||||||||||||||||||
| 言語 | ja | |||||||||||||||||||||||||||||
| 関連名称 | 10.3390/biom10050665 | |||||||||||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||||||||||
| 出版タイプ | VoR | |||||||||||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||||||||||||||||||
| タイトル | ||||||||||||||||||||||||||||||
| タイトル | Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning | |||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 著者 |
楠瀬, 賢也
× 楠瀬, 賢也
WEKO
231
× 芳賀, 昭弘× イノウエ, ミズキ
× 福田, 大受× 山田, 博胤
WEKO
197
× 佐田, 政隆
WEKO
309
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| 抄録 | ||||||||||||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||||||||||||
| 内容記述 | A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data. | |||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||||||||||||
| 主題 | echocardiography | |||||||||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||||||||||||
| 主題 | artificial intelligence | |||||||||||||||||||||||||||||
| キーワード | ||||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||||||||||||
| 主題 | view classification | |||||||||||||||||||||||||||||
| 書誌情報 |
en : Biomolecules 巻 10, 号 5, p. 665, 発行日 2020-04-25 |
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| 収録物ID | ||||||||||||||||||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||||||||||||||||||
| 収録物識別子 | 2218273X | |||||||||||||||||||||||||||||
| 出版者 | ||||||||||||||||||||||||||||||
| 出版者 | MDPI | |||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 権利情報 | ||||||||||||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||||||||||
| 権利情報 | © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | |||||||||||||||||||||||||||||
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| 識別子 | 366093 | |||||||||||||||||||||||||||||
| 識別子タイプ | URI | |||||||||||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||||||||||
