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Precision Medicine DTP - Realising the potential of retinal image analysis with AI methods for monitoring brain health in the community

   College of Medicine and Veterinary Medicine

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  Dr M Bernabeu, Prof Baljean Dhillon  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project


Changes in the brain leading to brain disease are thought to start decades before cognitive symptoms emerge. If biomarkers for these early stages could be identified, it would contribute to a more accurate estimation of an individual’s risk of developing disease and enable the monitoring of high-risk (presymptomatic) persons as well as providing the means for assessing the efficacy of new interventions. The retina is an extension of the brain sharing embryological origins as well as a blood supply and nerve tissue. It therefore has huge potential as a site for biomarker investigation through easy, noninvasive imaging, and computational image analysis to reveal valuable information about microvascular health, deposition, and neurodegenerative damage1.

The Scottish Collaborative Optometry-Ophthalmology Network e-research (SCONe, is a pioneering project established in 2020 which aims to build a world-leading nationwide retinal image resource for innovation in eye research and healthcare. In Scotland, community-based optometrists routinely collect millions of retinal images every year and many have been doing so for more than a decade. This represents a unique opportunity to create a large-scale longitudinal image resource that is representative of the primary care population. Within its initial two-year pilot phase, SCONe demonstrated the feasibility of bringing community-acquired retinal images for people aged 60+ together with other routinely collected healthcare data within the NHS National Safe Haven and has successfully acquired and linked over 200,000 images.

Community-based early identification of individuals at risk of cognitive decline and dementia is currently a major unmet clinical need. Our solution is to exploit the potential embedded within retinal images to predict neurovascular health in the aging brain using routinely captured retinal images, cross-linked hospital datasets with information on symptoms/diagnosis, and the latest advances in Artificial Intelligence2. Based on this, we will devise a retinal neurovascular biomarker toolkit for cognitive decline, stroke, and dementia. Predictive modelling based on routinely collected retinal images poses a broad range of novel image analysis problems related to image quality control and enhancement, longitudinal image registration, and feature engineering3.


The main aim of the study is to develop Artificial Intelligence approaches to the early identification of individuals at risk of cognitive decline and dementia, a major unmet clinical need, that can be deployed in the community (GP practices, optometrists, consumer devices). 

Progress towards this aim will be delivered based on the following objectives:

  1. Taking advantage of the data linkage capabilities of Public Health Scotland, link the SCONe repository of retinal image with hospital inpatient/outpatient electronic health records (e.g. SMR datasets) to identify a population of individuals over 60 years old with retinal images spanning over 10 years or more and incident diagnosis of cognitive decline, stroke or dementia.
  2. Develop novel approaches to quality scoring and retinal image enhancement using generative adversarial networks (GANs) to make this community acquired dataset suitable for latest advances in deep learning for image classification.
  3. Develop novel approaches to longitudinal retinal image registration and identification of key retinal phenotypes changing over time.
  4. Extend existing epidemiological models predicting the risk of brain health deterioration and incident disease with the SCONe linked retinal images, either in raw format at multiple points in time or after having characterised their temporal evolution in point 3. 

Training outcomes

The student will receive state-of-the-art training in the core disciplines of image analysis, computational modelling, and data science while gaining expert knowledge in the context of brain disease and in neuroscience more generally. This highly interdisciplinary approach is well aligned with the “T-shaped researcher” training requirements identified as key in the DTP.

Q&A Session

If you have any questions regarding this project, you are invited to attend a Q&A  session hosted by the Supervisor(s) on 9th December at 10.30am via Microsoft Teams. Click here to join the meeting. If you get an error message when accessing the link, please try a different device.

About the Programme

This MRC programme is joint between the Universities of Edinburgh and Glasgow. You will be registered at the host institution of the primary supervisor detailed in your project selection.

All applications should be made via the University of Edinburgh, irrespective of project location. For those applying to a University of Glasgow project, your application along with any supporting documents will be shared with University of Glasgow.

Please note, you must apply to one of the projects and you must contact the primary supervisor prior to making your application. Additional information on the application process is available from the following link:  

For more information about Precision Medicine visit:

Funding Notes

Start: September 2023

Qualifications criteria: Applicants applying for an MRC DTP in Precision Medicine studentship must have obtained, or will soon obtain, a first or upper-second class UK honours degree or equivalent non-UK qualification, in an appropriate science/technology area. The MRC DTP in Precision Medicine grant provides tuition fees and stipend of at least £17,668 (UKRI rate 2022/23).

Full eligibility details are available:

Enquiries regarding programme: [Email Address Removed]


1 McGrory, S. et al. Alzheimer’s Dement. (Amsterdam, Netherlands) 6, 91–107 (2017)
2 Ting, D. S. W. et al. Progress in Retinal and Eye Research 72, 100759 (2019)
3 Mookiah, M. R. K. et al. Medical Image Analysis 68, 101905 (2021)

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