Virtual Biopsy with the Eye - Using Machine Learning to Detect and Track Chronic Kidney Disease (CKD)

The aim of this project is to use the full range of retinal imaging techniques available - Ultra-Wide Field Scanning Laser Opthalmoscopes (UWF-SLO, cameras manufactured by Optos, the industrial partner of this project), Optical Coherence Tomography (OCT), and OCT-Angiography (OCTA) - to build a detailed 3D image of the retinal vasculature. Using machine learning, I will then analyse and segment these 3D images from a rich and thorough clinical dataset acquired by CVS here at UoE for metrics and markers of vascular health.
The primary aim of this segmentation and analysis is to detect and track CKD by measuring choroidal thickness and volume (previously only thickness at a discrete set of points was measured) - building on the work of my supervisor team in their paper "Chorioretinal thinning in chronic kidney disease links to inflammation and endothelial dysfunction" - but this detailed retinal segmentation and vascular tracking will also hopefully enable other such investigations, and enable us to improve existing retinal vascular health metrics.
CKD currently affects 6-11% of the world's population, and being able to track progression and treatment efficacy is important to improve patient quality of life. Retinal imaging is non-invasive and relatively quick, thus would be a preferable clinical alternative to invasive kidney biopsies and slow lab tests on blood and urine samples, particularly if the process could be automated i.e. by training a classifier using machine learning.