Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from The Maastricht Study

Abstract

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.

A Systematic Review and Meta-Analysis of Applying Deep Learning in the Prediction of the Risk of Cardiovascular Diseases From Retinal Images

“… 32 , 38 Among the studies that predicted the presence of diabetes, the models had an AUROC ranging from 0.731 to 0.923 using retinal images only, which increased to 0.929 by adding clinical data. 23 , 28 , 33 , 38 , 40 The MAE ranged from 8.96 to 11.35 mm Hg for systolic blood pressure prediction, 6.42 to 6.84 mm Hg for diastolic blood pressure, and 3.29 to 4.31 kg/m 2 for BMI prediction. 26 , 32 The AUROC for prediction of prevalent CKD was between 0.911 and 0.918 and was up to 0.938 when clinical metadata was added to the model.…”