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Direct Classification of Type 2 Diabetes From Retinal Pundus Images in a Population-based Sample From The Maastricht Study

机译：从基于马斯特里赫特研究的人群样本中视网膜充盈图像直接分类2型糖尿病

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Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output retinal biomarkers as well as T2D works best (AUC = 0.746 [±0.001]). Furthermore, the classification performance can be improved when images with high prediction uncertainty are referred to a specialist. We also show that the combination of images of the left and right eye per individual can further improve the classification performance (AUC = 0.758 [±0.003]). using a simple averaging approach. The results are promising, suggesting the feasibility of screening for T2D from retinal fundus images.
机译：2型糖尿病（T2D）是一种慢性代谢性疾病，可能导致失明和心血管疾病。有关早期T2D的信息可能存在于视网膜眼底图像中，但这些图像可用于多大程度的筛查背景仍是未知的。在这项研究中，深度神经网络被用来区分有和没有T2D的个体的眼底图像。我们研究了三种实现高分类性能的方法，这些方法通过接收器工作曲线（ROC-AUC）下的面积来衡量。同时输出视网膜生物标志物和T2D的多目标学习方法效果最好（AUC = 0.746 [±0.001]）。此外，当将具有高预测不确定性的图像推荐给专家时，可以提高分类性能。我们还显示，每个人的左眼和右眼图像的组合可以进一步改善分类性能（AUC = 0.758 [±0.003]）。使用简单的平均方法。结果是有希望的，表明从视网膜眼底图像筛查T2D的可行性。

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