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Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead toblindness and cardiovascular disease. Information about early stage T2D mightbe present in retinal fundus images, but to what extent these images can beused for a screening setting is still unknown. In this study, deep neuralnetworks were employed to differentiate between fundus images from individualswith and without T2D. We investigated three methods to achieve highclassification performance, measured by the area under the receiver operatingcurve (ROC-AUC). A multi-target learning approach to simultaneously outputretinal biomarkers as well as T2D works best (AUC = 0.746 [$\pm$0.001]).Furthermore, the classification performance can be improved when images withhigh prediction uncertainty are referred to a specialist. We also show that thecombination of images of the left and right eye per individual can furtherimprove the classification performance (AUC = 0.758 [$\pm$0.003]), using asimple averaging approach. The results are promising, suggesting thefeasibility of screening for T2D from retinal fundus images.

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