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

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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 MethodologyThe 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 strategiesStrongly 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) CollaborationsThis 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.