Predicting the future development of advanced age-related macular degeneration (AMD) using multi-modal imaging and genetics

1) Description and potential impact of the research

Age-related macular degeneration (AMD) is the most common cause of blindness in the developed world. In the UK, more than 200 people develop the advanced blinding neovascular ("wet") form of the disease daily. Wet AMD typically affects one eye first, leaving patients reliant upon the unaffected "good" eye to allow the activities of daily living. Unfortunately, in many - but not all - patients, the good eye subsequently becomes affected, and the patient becomes severely sight impaired.

As a result, a number of studies have begun to explore preventative therapies for the development of AMD progression, both in its wet and dry forms. In many cases, these treatments are invasive, with the potential for adverse effects. Robust methods for predicting future progression of AMD would thus allow better targeting of these therapies - such risk stratification could allow identification of those patients at risk of imminent conversion (i.e., development of advanced AMD within a six-month period) as well as those patients that could be reassured (i.e., unlikely to develop advanced AMD within the next two years).

2) Objectives

- Develop machine learning systems for prediction of imminent AMD progression, defined as progression to choroidal neovascularization (CNV) or geographic atrophy (GA) within a 6-month period, using demographic and clinical metadata plus multi-modality imaging (colour fundus photography (CFP) / fundus autofluorescence (FAF), and high-resolution 3D optical coherence tomography (OCT)) from: 1) single time-point, and 2) longitudinal data.
- Develop AMD "patient reassurance" models, defined as NO progression to choroidal neovascularization (CNV) or geographic atrophy (GA) within a 2-year period, using the same data.
- Incorporate information on genetic variants and/or other diagnostic tests into AMD prediction models and evaluate its incremental effects on model performance in each clinical scenario.
- Benchmark AMD prediction models against performance of human experts (ophthalmologists with subspecialty expertise in retinal disease at Moorfields Eye Hospital).
- Explore preliminary clinical translation by validating model performance in prospective, non-interventional clinical studies.

3) Novelty of Research Methodology

The use of machine learning has shown great potential for retinal disease classification using imaging modalities. A number of studies have demonstrated the potential of deep learning to predict future AMD progression using retinal CFP and/or OCT scans. However, these studies have typically employed a single modality at a single time-point, producing good - but not spectacular - results.

We will develop AMD progression models that incorporate longitudinal, multi-modal imaging data, and genetic data, and then demonstrate their potential clinical applicability. This project will involve the application of established technologies such as convolutional neural networks, as well as newer approaches such as graph neural networks. It will also involve more advanced modelling techniques such as neural ordinary differential equations. Lastly, it will involve both supervised and semi-supervised learning.

4) Alignment to EPSRC's strategies

Strongly related to EPSRC's "Medical Imaging" research area and EPSRC's Healthcare Technology challenge to "Optimise Treatment and Care through effective diagnosis, patient-specific prediction and evidence-based intervention."

5) Collaborations
This project will use imaging data from Moorfields Eye Hospital. Affiliated with the UCL Institute of Ophthalmology, Moorfields has the world's largest single-centre ophthalmic imaging database (including >200,000 paired CFP and OCT scans from nearly 10,000 patients with advanced AMD). Since 2019, they have begun collecting gene they have begun collecting genetic data on these patients also.

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.