| Item type |
文献 / Documents(1) |
| 公開日 |
2021-02-18 |
| 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/10.1155/2018/1875431 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1155/2018/1875431 |
| 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 |
Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy |
|
言語 |
en |
| 著者 |
ナガサト, ダイスケ
タブチ, ヒトシ
オオスギ, ヒデハル
マスモト, ヒロキ
エンノ, ヒロキ
イシトビ, ナオフミ
ソノベ, トモアキ
カメオカ, マサヒロ
仁木, 昌徳
ハヤシ, ケン
三田村, 佳典
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
The aim of this study is to assess the performance of two machine-learning technologies, namely, deep learning (DL) and support vector machine (SVM) algorithms, for detecting central retinal vein occlusion (CRVO) in ultrawide-field fundus images. Images from 125 CRVO patients (n = 125 images) and 202 non-CRVO normal subjects (n = 238 images) were included in this study. Training to construct the DL model using deep convolutional neural network algorithms was provided using ultrawide-field fundus images. The SVM uses scikit-learn library with a radial basis function kernel. The diagnostic abilities of DL and the SVM were compared by assessing their sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic curve for CRVO. For diagnosing CRVO, the DL model had a sensitivity of 98.4% (95% confidence interval (CI), 94.3–99.8%) and a specificity of 97.9% (95% CI, 94.6–99.1%) with an AUC of 0.989 (95% CI, 0.980–0.999). In contrast, the SVM model had a sensitivity of 84.0% (95% CI, 76.3–89.3%) and a specificity of 87.5% (95% CI, 82.7–91.1%) with an AUC of 0.895 (95% CI, 0.859–0.931). Thus, the DL model outperformed the SVM model in all indices assessed (P < 0.001 for all). Our data suggest that a DL model derived using ultrawide-field fundus images could distinguish between normal and CRVO images with a high level of accuracy and that automatic CRVO detection in ultrawide-field fundus ophthalmoscopy is possible. This proposed DL-based model can also be used in ultrawide-field fundus ophthalmoscopy to accurately diagnose CRVO and improve medical care in remote locations where it is difficult for patients to attend an ophthalmic medical center. |
|
言語 |
en |
| 書誌情報 |
en : Journal of Ophthalmology
巻 2018,
p. 1875431,
発行日 2018-11-01
|
| 収録物ID |
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|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2090004X |
| 収録物ID |
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|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
20900058 |
| 出版者 |
|
|
出版者 |
Hindawi |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
© 2018 Daisuke Nagasato et al. This is an open access article distributed under the Creative Commons Attribution License(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| EID |
|
|
識別子 |
351519 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
jo_2018_1875431.pdf (1.34 MB)
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