Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks
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DOI：
10.1001/jamaophthalmol.2017.3782
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发表时间：
2017-11-01
影响因子：
8.1
通讯作者：
Bressler, Neil M.
中科院分区：
文献类型：
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作者：
Burlina, Philippe M.;Joshi, Neil;Bressler, Neil M.
重要性老年性黄斑变性(AMD)影响着全世界数百万人。中期可能没有被发现，因为它通常是无症状的。然而，AMD的首选实践模式建议识别疾病的这一阶段的个体，以教育如何在发生实质性视力丧失之前监测脉络膜新生血管阶段的早期检测，并考虑可能降低疾病从中级进展到晚期的风险的饮食补充剂。目的开发利用深度学习方法从眼底图像中自动检测AMD的方法，将深度学习方法应用于对这些图像的自动评估，并利用人工智能的进步。设计、设置和参与者经过明确训练以执行AMD自动分级的深度卷积神经网络与使用转移学习和通用特征的替代深度学习方法以及训练有素的临床分级者进行了比较。将年龄相关性黄斑变性自动检测应用于一个两类分类问题，该问题的任务是区分无病/早期和可参考的中晚期。使用几个需要不同数据分区的实验，评估了机器算法和人类评分器在评估超过13万张图像时的性能，这些图像根据年龄、性别和种族/民族从4613名患者中确定，对照包括在美国国立卫生研究院年龄相关眼病研究数据集中的黄金标准。MAIN结果和测量准确性、接收者操作特征和曲线下面积，以及。结果深层卷积神经网络方法的准确率为88.4%(SD，0.5%)~91.6%(SD，0.1%)，受试者工作特征曲线下面积在0.94~0.96之间。(SD)在0.764(0.010)和0.829(0.003)之间，这表明与黄金标准年龄相关眼病研究数据集基本一致。结论和相关性应用基于深度学习的眼底图像自动评估AMD可以产生类似于人类表现水平的结果。这项研究表明，自动化算法可以在AMD的当前管理中发挥独立于专家人类评分员的作用，并可以解决筛查或监测、获得医疗保健的成本以及针对AMD发展或进展的新疗法的评估。
IMPORTANCE Age-related macular degeneration (AMD) affects millions of people throughout the world. The intermediate stage may go undetected, as it typically is asymptomatic. However, the preferred practice patterns for AMD recommend identifying individuals with this stage of the disease to educate how to monitor for the early detection of the choroidal neovascular stage before substantial vision loss has occurred and to consider dietary supplements that might reduce the risk of the disease progressing from the intermediate to the advanced stage. Identification, though, can be time-intensive and requires expertly trained individuals.OBJECTIVE To develop methods for automatically detecting AMD from fundus images using a novel application of deep learning methods to the automated assessment of these images and to leverage artificial intelligence advances.DESIGN, SETTING, AND PARTICIPANTS Deep convolutional neural networks that are explicitly trained for performing automated AMD grading were compared with an alternate deep learning method that used transfer learning and universal features and with a trained clinical grader. Age-related macular degeneration automated detection was applied to a 2-class classification problem in which the task was to distinguish the disease-free/early stages from the referable intermediate/advanced stages. Using several experiments that entailed different data partitioning, the performance of the machine algorithms and human graders in evaluating more than 130 000 images that were deidentified with respect to age, sex, and race/ethnicity from 4613 patients against a gold standard included in the National Institutes of Health Age-Related Eye Disease Study data set was evaluated.MAIN OUTCOMES AND MEASURES Accuracy, receiver operating characteristics and area under the curve, and. score.RESULTS The deep convolutional neural network method yielded accuracy that ranged between 88.4%(SD, 0.5%) and 91.6%(SD, 0.1%), the area under the receiver operating characteristic curve was between 0.94 and 0.96, and. (SD) between 0.764 (0.010) and 0.829 (0.003), which indicated a substantial agreement with the gold standard Age-Related Eye Disease Study data set.CONCLUSIONS AND RELEVANCE Applying a deep learning-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels. This study demonstrates that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.