Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy
Deep Neural Network-Based Method for Detecting Central Retinal Vein Occlusion Using Ultrawide-Field Fundus Ophthalmoscopy
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DOI：
10.1155/2018/1875431
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发表时间：
2018-01-01
影响因子：
1.9
通讯作者：
Mitamura, Yoshinori
中科院分区：
文献类型：
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作者：
Nagasato, Daisuke;Tabuchi, Hitoshi;Mitamura, Yoshinori
本研究的目的是评估两种机器学习技术，即深度学习算法和支持向量机算法，用于检测超广域眼底图像中的视网膜中央静脉阻塞(CRVO)的性能。本研究包括125例CRVO患者(125张图像)和202例非CRVO正常人(238张图像)的图像。使用超广域眼底图像提供了使用深度卷积神经网络算法来构建DL模型的训练。支持向量机使用带有径向基函数核的SCRKIT-LEARN库。比较DL和SVN4对CRVO诊断的敏感性、特异性和受试者工作特征曲线下面积(AUC)。DL模型诊断CRVO的敏感性为98.4%(95%可信区间，94.3-99.8%)，特异性为97.9%(95%可信区间，94.6-99.1%)，AUC值为0.989(95%可信区间，0.980-0.999)。而支持向量机模型的灵敏度为0‘1’84.0%(95%CI，76.3-89.3%)，特异度为87.5%(95%CI，82.7-91.1%)，AUC为0.895(95%CI，0.859-0.931)。因此，在评估的所有指标中，DL模型的表现都好于支持向量机模型(P<；全部为0.001)。我们的数据表明，使用超广域眼底图像导出的DL模型可以高精度地区分正常图像和CRVO图像，并且在超广域眼底检眼镜中自动检测CRVO是可能的。这种基于DL的模型还可以用于超广域眼底检眼镜检查，以准确诊断CRVO，并改善患者难以前往眼科医疗中心的偏远地区的医疗保健。
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 SVN4 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 0'1'84.0% (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.