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Approved research

iCor: stratification of patients with cardiovascular risk using AI-powered automatic retinal imaging as a surrogate for Small Vessel Disease

Principal Investigator: Mr Gabriel Reines March
Approved Research ID: 60857
Approval date: May 4th 2020

Lay summary

Small Vessel Disease (SVD) contributes to chronic health problems, including coronary heart disease, stroke and dementia. Due to their size, small vessels of the heart and brain cannot be visualised by clinicians with standard imaging tools, and therefore SVD is difficult to detect and diagnose. However, the blood vessels in the eye share many features with the heart and brain microvasculature, and they are easy to image non-invasively. Measurements taken on the eye's vessels can be related to those of the heart, and therefore used to detect SVD. The aim of this project is to develop Artificial Intelligence (AI) tools that will automatically analyse images of the back of the eye (retina) and aid the clinician make a better-informed decision on whether that patient has SVD. The number of cases for coronary heart disease, stroke and dementia is globally increasing, which puts a significant strain on NHS resources. Therefore, new healthcare strategies are needed to reduce this burden. Using the small vessels in the eye as a substitute to diagnose SVD in the heart is an appealing possibility, as taking an image of the back of the eye is relatively straightforward. Novel imaging technologies, such as Optical Coherence Tomography - Angiography (OCT-A) allow clinicians to visualise deep vessels of the eye with unprecedented detail. Having a system that automatically analyses these images would allow patients with significant retinal disease, and therefore at risk of developing SVD, to be referred to specialist care. If successful, this project would enable affordable, straightforward screening of retinal SVD in a variety of specialist and non-specialist clinical settings.