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ID 114346
Title Alternative
Automated detection of retinal nonperfusion area caused by retinal vein occlusion
Author
Nagasato, Daisuke Tsukazaki Hospital
Tabuchi, Hitoshi Tsukazaki Hospital
Masumoto, Hiroki Tsukazaki Hospital
Enno, Hiroki Rist Incorporated
Ishitobi, Naofumi Tsukazaki Hospital
Kameoka, Masahiro Tsukazaki Hospital
Content Type
Journal Article
Description
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.
Journal Title
PLOS ONE
ISSN
19326203
Publisher
PLOS
Volume
14
Issue
11
Start Page
e0223965
Published Date
2019-11-07
Rights
© 2019 Nagasato et al. This is an open access article distributed under the terms of 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 author and source are credited.
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language
eng
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departments
Medical Sciences