| Item type |
文献 / Documents(1) |
| 公開日 |
2020-03-19 |
| 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/10.1371/journal.pone.0223965 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1371/journal.pone.0223965 |
| 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 |
Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning |
|
言語 |
en |
| タイトル別表記 |
|
|
その他のタイトル |
Automated detection of retinal nonperfusion area caused by retinal vein occlusion |
|
言語 |
en |
| 著者 |
ナガサト, ダイスケ
タブチ, ヒトシ
マスモト, ヒロキ
エンノ, ヒロキ
イシトビ, ナオフミ
カメオカ, マサヒロ
仁木, 昌徳
三田村, 佳典
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
We aimed to assess the ability of deep learning (DL) and support vector machine (SVM) to detect a nonperfusion area (NPA) caused by retinal vein occlusion (RVO) with optical coherence tomography angiography (OCTA) images. The study included 322 OCTA images (normal: 148; NPA owing to RVO: 174 [128 branch RVO images and 46 central RVO images]). Training to construct the DL model using deep convolutional neural network (DNN) algorithms was provided using OCTA images. The SVM used a scikit-learn library with a radial basis function kernel. The area under the curve (AUC), sensitivity and specificity for detecting an NPA were examined. We compared the diagnostic ability (sensitivity, specificity and average required time) between the DNN, SVM and seven ophthalmologists. Heat maps were generated. With regard to the DNN, the mean AUC, sensitivity, specificity and average required time for distinguishing RVO OCTA images with an NPA from normal OCTA images were 0.986, 93.7%, 97.3% and 176.9 s, respectively. With regard to SVM, the mean AUC, sensitivity, and specificity were 0.880, 79.3%, and 81.1%, respectively. With regard to the seven ophthalmologists, the mean AUC, sensitivity, specificity and average required time were 0.962, 90.8%, 89.2%, and 700.6 s, respectively. The DNN focused on the foveal avascular zone and NPA in heat maps. The performance of the DNN was significantly better than that of SVM in all parameters (p < 0.01, all) and that of the ophthalmologists in AUC and specificity (p < 0.01, all). The combination of DL and OCTA images had high accuracy for the detection of an NPA, and it might be useful in clinical practice and retinal screening. |
|
言語 |
en |
| 書誌情報 |
en : PLOS ONE
巻 14,
号 11,
p. e0223965,
発行日 2019-11-07
|
| 収録物ID |
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|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
19326203 |
| 出版者 |
|
|
出版者 |
PLOS |
|
言語 |
en |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
© 2019 Nagasato et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| EID |
|
|
識別子 |
361647 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
pone_14_11_e0223965.pdf (2.99 MB)
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