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ID 115524
著者
Nagasato, Daisuke Tsukazaki Hospital
Tabuchi, Hitoshi Tsukazaki Hospital
Ohsugi, Hideharu Tsukazaki Hospital
Masumoto, Hiroki Tsukazaki Hospital
Enno, Hiroki Rist Inc.
Ishitobi, Naofumi Tsukazaki Hospital
Sonobe, Tomoaki Tsukazaki Hospital
Kameoka, Masahiro Tsukazaki Hospital
Hayashi, Ken Hayashi Eye Hospital
資料タイプ
学術雑誌論文
抄録
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.
掲載誌名
Journal of Ophthalmology
ISSN
2090004X
20900058
出版者
Hindawi
2018
開始ページ
1875431
発行日
2018-11-01
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.
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出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
医学系