Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.
Development of a deep residual learning algorithm to screen for glaucoma from fundus photography.
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DOI：
10.1038/s41598-018-33013-w
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发表时间：
2018-10-02
影响因子：
4.6
通讯作者：
Asaoka R
中科院分区：
文献类型：
--
作者：
Shibata N;Tanito M;Mitsuhashi K;Fujino Y;Matsuura M;Murata H;Asaoka R
该研究的目的是开发一种深度残差学习算法，以通过眼底摄影筛查青光眼，并与眼科住院医师相比，测量其诊断性能。训练数据集由1，364张具有昏迷指示的彩色眼底照片和1，768张没有昏迷特征的彩色眼底照片组成。测试数据集由60名青光眼患者的60只眼和50名正常人的50只眼组成。使用训练数据集，开发了一种称为图像识别深度残差学习（ResNet）的深度学习算法来区分青光眼，并使用受试者工作特征曲线（AROC）下的面积在测试数据集中验证了其诊断准确性。使用训练数据集构建了用于图像识别的深度残差学习，并使用测试数据集进行了验证。三名眼科住院医师也证实了测试数据集中青光眼的存在。与眼科住院医师相比，深度学习算法的诊断性能显著更高;使用ResNet，所有测试数据的AROC为96.5（95%置信区间[CI]：93.5至99.6）%，而三位住院医师获得的AROC在72.6%至91.2%之间。
The Purpose of the study was to develop a deep residual learning algorithm to screen for glaucoma from fundus photography and measure its diagnostic performance compared to Residents in Ophthalmology. A training dataset consisted of 1,364 color fundus photographs with glaucomatous indications and 1,768 color fundus photographs without glaucomatous features. A testing dataset consisted of 60 eyes of 60 glaucoma patients and 50 eyes of 50 normal subjects. Using the training dataset, a deep learning algorithm known as Deep Residual Learning for Image Recognition (ResNet) was developed to discriminate glaucoma, and its diagnostic accuracy was validated in the testing dataset, using the area under the receiver operating characteristic curve (AROC). The Deep Residual Learning for Image Recognition was constructed using the training dataset and validated using the testing dataset. The presence of glaucoma in the testing dataset was also confirmed by three Residents in Ophthalmology. The deep learning algorithm achieved significantly higher diagnostic performance compared to Residents in Ophthalmology; with ResNet, the AROC from all testing data was 96.5 (95% confidence interval [CI]: 93.5 to 99.6)% while the AROCs obtained by the three Residents were between 72.6% and 91.2%.
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