Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.
Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks.
复制标题
使用深度神经网络自动检测视网膜照片中的 39 种眼底疾病和状况
DOI：
10.1038/s41467-021-25138-w
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发表时间：
2021-08-10
影响因子：
16.6
通讯作者：
Zhang M
中科院分区：
文献类型：
--
作者：
Cen LP;Ji J;Lin JW;Ju ST;Lin HJ;Li TP;Wang Y;Yang JF;Liu YF;Tan S;Tan L;Li D;Wang Y;Zheng D;Xiong Y;Wu H;Jiang J;Wu Z;Huang D;Shi T;Chen B;Yang J;Zhang X;Luo L;Huang C;Zhang G;Huang Y;Ng TK;Chen H;Chen W;Pang CP;Zhang M
如果没有及时的诊断和适当的治疗，视网膜眼底疾病会导致不可逆转的视力损害。基于单一疾病的深度学习算法已被开发用于检测糖尿病视网膜病变、年龄相关性黄斑变性和青光眼。在这里，我们开发了一个深度学习平台(DLP)，能够通过使用来自不同来源的249,620张带有275,543个标签的眼底图像来检测多种常见的可参考的眼底疾病和状况(39类)。我们的DLP在原始测试数据集中实现了多标签分类的频率加权平均F1分数0.923，敏感度0.978，特异度0.996，接收器工作特征曲线下面积0.9984，达到了视网膜专家的平均水平。外部多医院测试、公共数据测试和远程阅读应用也表明，对多种视网膜疾病和情况的检测效率很高。这些结果表明，我们的DLP可以应用于视网膜眼底疾病的分诊，特别是在世界各地的偏远地区。
Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.
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