Recent studies have demonstrated the potential of OCTA retinal imaging for the discovery of biomarkers of vascular disease of the eye and other organs. Furthermore, advances in deep learning have made it possible to train algorithms for the automated detection of such biomarkers. However, two key limitations of this approach are the need for large numbers of labeled images to train the algorithms, which are often not met by the typical single-centre prospective studies in the literature, and the lack of interpretability of the features learned during training. In the current study, we developed a network analysis framework to characterise retinal vasculature where geometric and topological information are exploited to increase the performance of classifiers trained on tens of OCTA images. We demonstrate our approach in two different diseases with a retinal vascular footprint: diabetic retinopathy (DR) and chronic kidney disease (CKD). Our approach enables the discovery of previously unreported retinal vascular morphological differences in DR and CKD, and demonstrate the potential of OCTA for automated disease assessment.
OCTA imaging is an emerging modality for the discovery of retinal biomarkers in systemic disease. Several studies have already shown the potential of deep learning algorithms in the medical domain. However, they generally require large amount of manually graded images which may not always be available. In our study, we aim to investigate whether transfer learning can help in identifying patient status from a relatively small dataset. Additionally, we explore if data augmentation may help in improving our classification accuracy. Finally, for the first time, we propose a validation of our model on OCTA images acquired with a different device. OCTA scans from three different groups of participants were analysed: diabetic with and without retinopathy (DR and NoDR, respectively) and healthy subjects. We used the convolutional neural network architecture VGG16 and achieved 83.29% accuracy when classifying DR, NoDR and Controls. Our results demonstrate how transfer learning enables fairly accurate OCTA scan classification and augmentation based on geometric transformations helps in improving the classification accuracy further. Finally, we show how our model maintains consistent performance across OCTA imaging devices, without any re-training.
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