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.

Original languageEnglish
Title of host publicationMedical Imaging 2020: Computer-Aided Diagnosis
PublisherSPIE-Society of Photo-Optical Instrumentation Engineers
Number of pages6
Publication statusPublished - 2020
EventConference on Medical Imaging - Computer-Aided Diagnosis - Houston, United States
Duration: 16 Feb 202019 Feb 2020

Publication series

SeriesProceedings of SPIE - The International Society for Optical Engineering


ConferenceConference on Medical Imaging - Computer-Aided Diagnosis
Country/TerritoryUnited States


  • Deep Learning
  • Retinal Image Analysis
  • Type 2 Diabetes
  • Classification Uncertainty
  • The Maastricht Study

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