Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
Artificial Intelligence to Identify Retinal Fundus Images, Quality Validation, Laterality Evaluation, Macular Degeneration, and Suspected Glaucoma
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DOI：
10.2147/opth.s235751
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发表时间：
2020-01-01
影响因子：
2.2
通讯作者：
Labarta, Jesus
中科院分区：
文献类型：
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作者：
Angel Zapata, Miguel;Royo-Fibla, Didac;Labarta, Jesus
目的：为了评估深度学习算法在视网膜眼底图像中执行不同任务的性能：（1）相对于光学相干断层扫描（OCT）或其他图像的视网膜眼底图像的检测，（2）高质量视网膜眼底图像的评估，（3）右眼（OD）和左眼（OS）视网膜眼底图像之间的区分，（4）年龄相关性黄斑变性（AMD）的检测和（5）可诊断性青光眼性视神经病变（GON）的检测。回顾性研究来自306，302张图像的数据库，Optretina的标记数据集。三个不同的眼科医生，都是视网膜专家，对所有图像进行了分类。数据集按患者分为训练（80%）和测试（20%）部分。采用了三种不同的CNN架构，其中两种是定制设计的，以最大限度地减少参数数量，同时对其准确性的影响最小。结果：眼底图像检测的曲线下面积（AUC）为0.979，准确性为96%，敏感性为97.7%，特异性为92.4%。判定眼底图像质量好的AUC为0.947，准确率为91.8%（敏感性96.9%，特异性81.8%）。OD/OS算法的AUC为0.989，准确率为97.4%。AMD的AUC为0.936，准确率为86.3%（灵敏度90.2%特异性82.5%），GON的AUC为0.863，准确率为80.2%（灵敏度76.8%特异性83.8%）。结论：深度学习算法可以区分视网膜眼底图像和其他图像。算法可以评估图像的质量，区分右眼或左眼，并以高水平的准确性、灵敏度和特异性检测AMD和GON的存在。
Purpose: To assess the performance of deep learning algorithms for different tasks in retinal fundus images: (1) detection of retinal fundus images versus optical coherence tomography (OCT) or other images, (2) evaluation of good quality retinal fundus images, (3) distinction between right eye (OD) and left eye (OS) retinal fundus images,(4) detection of age-related macular degeneration (AMD) and (5) detection of referable glaucomatous optic neuropathy (GON).Patients and Methods: Five algorithms were designed. Retrospective study from a database of 306,302 images, Optretina's tagged dataset. Three different ophthalmologists, all retinal specialists, classified all images. The dataset was split per patient in a training (80%) and testing (20%) splits. Three different CNN architectures were employed, two of which were custom designed to minimize the number of parameters with minimal impact on its accuracy. Main outcome measure was area under the curve (AUC) with accuracy, sensitivity and specificity.Results: Determination of retinal fundus image had AUC of 0.979 with an accuracy of 96% (sensitivity 97.7%, specificity 92.4%). Determination of good quality retinal fundus image had AUC of 0.947, accuracy 91.8% (sensitivity 96.9%, specificity 81.8%). Algorithm for OD/OS had AUC 0.989, accuracy 97.4%. AMD had AUC of 0.936, accuracy 86.3% (sensitivity 90.2% specificity 82.5%), GON had AUC of 0.863, accuracy 80.2% (sensitivity 76.8%, specificity 83.8%).Conclusion: Deep learning algorithms can differentiate a retinal fundus image from other images. Algorithms can evaluate the quality of an image, discriminate between right or left eye and detect the presence of AMD and GON with a high level of accuracy, sensitivity and specificity.