Developing new diabetic retinopathy biomarkers through image processing, computational modelling, and machine learning.

The aim of this project is to investigate clinically relevant diabetic retinopathy biomarkers and design the computational pipelines and protocols necessary to facilitate their use in future large-scale clinical and pre-clinical studies.

Diabetic retinopathy (DR) is the leading cause of visual loss in developed countries worldwide. Previous studies have reported structural and haemodynamic changes in the diabetic eye microvasculature that precede clinically manifested pathological alterations of the retina. Therefore, new methods that allow greater understanding of these early changes may empower earlier detection of DR helping to limit damage to the retina and
vision function.

Optical Coherence Tomography Angiography (OCT-A) is a non-invasive, dyeless technique capable of resolving the microvasculature of the eye to a level of detail never
seen before. It is only very recently that the first OCT-A commercial products have started to be utilised in clinical centres. One of the first OCT-A devices installed in the UK is hosted in Edinburgh at the Clinical Research Imaging Centre (CRIC, a clinical research facility in partnership between the University of Edinburgh and NHS Lothian).

Recent works showed that it is possible to build computational blood flow models from high-resolution images of the parafoveal region of the retina (of paramount importance for sharp central vision and visual detail). These computational models can provide a detailed haemodynamic characterisation of the region beyond the capabilities of current imaging technologies.

The project involves design and run a series of longitudinal studies aimed at identifying morphological and flow-based early indicators of the pathological changes that lead to visual loss in advanced DR stages. In particular, image processing, computational modelling, and machine learning methods will be exploited in order to characterise the early changes in microvascular haemodynamics. Moreover, there is a unique opportunity to look at the genetic correlation between omics data and imaging data. Therefore, the project aims to combine these novel methodologies with the OCT-A datasets at Edinburgh and Belfast in order to realise the full potential of this patient-specific approach to achieving Precision Medicine for eye care.