Data driven public health approaches for diabetic retinopathy and age-related macular degeneration

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This fellowship will consist of data-driven projects building on the research strengths of the Centre for Public Health, QUB, focusing on use of electronic health records (EHR) and retinal imaging data to improve population eye health. Developing novel outcome measures for chronic eye disease:Management of the most common sight-threatening eye diseases in the UK, age-related macular degeneration (AMD) and glaucoma, requires regular monitoring and rapid treatment if disease progression accelerates. Changes in either ocular structures or visual function can signal that progression is occurring. There have been rapid advances in 3D retinal imaging technology, centred on a technique known as optical coherence tomography (OCT), that can resolve ocular structures in unprecedented detail. However, analytical methods to make full use of this information are lacking, especially when attempting to link structural and functional changes in the retina. The fellow will develop novel statistical methods to integrate a large retinal imaging dataset of AMD patients with measurements of visual function drawn from EHRs. This work will be conducted with QUB ophthalmologists and OCT experts (groups led by Prof Tunde Peto and Dr Ruth Hogg) and statisticians at City, University of London. The aim is to develop meaningful outcome measures of AMD progression for use in clinical trials of new treatments. Optimising diabetic retinopathy screening:Diabetic retinopathy (DR), one of the most common causes of sight loss among working-age people, occurs when high blood sugar damages blood vessels in the retina. Those at risk of DR are screened with retinal photographs taken at regular intervals. Images are manually graded for the presence of specific changes to identify those in need of treatment. The aim of this project is to explore the potential of integrating automated image analysis into the Northern Ireland DR screening programme to target treatment more effectively and reduce costs. New analytical approaches will be developed to fully exploit information contained within the programme's substantial screening libraries. A key challenge is predicting which patients will progress to sight-threatening DR in the short term. Treatment could be targeted towards this group, rather than towards the many patients that remain stable in the intermediate stages of the disease across multiple screenings. Accurate prediction of progression could also inform risk-based screening with longer screening intervals for stable patients, reducing the overall number of screening visits for the population and the associated costs.DR progression is difficult to predict using current methods. There may be subtle patterns of retinal changes predicting DR progression detectable only using automated approaches that can analyse data from thousands of patients simultaneously. The latest generation of machine learning techniques (deep learning algorithms) can almost match the ability of human graders to detect DR in retinal images. The next step will be to determine whether these techniques can be applied to predict progression of DR, leveraging the full set of information within image sequences. These will be drawn from the Northern Ireland DR screening programme databank (clinical lead, Prof Peto), a unique repository of retinal images for approximately 87,000 patients that has recently been centralised and linked backed as far as 2002. Working with Prof Peto and mathematicians at King's College London, the fellow will develop and apply the latest machine learning techniques to a large set of screening images to detect novel features predictive of DR progression. Northern Ireland is an ideal for this study as there is little migration among older people so patient outcomes can be monitored more easily than in other parts of the UK. Performance of the automated methods will be assessed along with the potential for improvements to the screening programme.