Smart Medical Imaging

EPSRC Centre for Doctoral Training

Research

2019_025 - Early detection and diagnosis of age-related macular degeneration AMD using machine learning

1st supervisor: Daniel Rueckert
2nd supervisor: Bernhard Kainz

AMD is the commonest cause of blindness in the elderly. By 2020, 200 million people are expected to be affected with AMD, increasing to nearly 300 million by 2040 [1]. It is a complex, heritable and heterogeneous disease that affects the macula, the central retina that is responsible for detailed central vision. The pathogenesis of AMD remains unclear. Drusen are an early clinical feature of the disease and are visualised as yellow deposits predominantly located throughout the macula. Currently, severity of AMD is classified as early, intermediate and late AMD based on the size and morphology of drusen and pigmentary changes at the macula. However, the inter-individual progression rates of AMD based on these characteristics are extremely variable.

Recent advances in imaging technologies have enabled identification of other imaging biomarkers of AMD such as reticular pseudodrusen (subretinal deposits) that develop and progress independent of drusen [2]. Optical coherence tomography (OCT) also indicates that there are 38 different types of drusen that may co-exist in a patient and their significance in AMD progression is unknown [3]. Furthermore, not all high-risk features in the retina progress to late AMD within an individual highlighting the need for computational analysis to better understand the trajectories of these markers. Similarly, late AMD may present as geographic atrophy (GA) with atrophy of photoreceptors and retinal pigment epithelium (RPE) in the macula or choroidal neovascularization (CNV) where choroidal blood vessels migrate into the retina and it remains unknown why, when and which marker/event precede or predict each form of late AMD. Therefore, there is an unmet need to better understand the pathophysiology of AMD, accurately classify AMD and develop personalised risk prediction models to progress into prevention trials for this condition.

The PhD project will be carried in collaboration with the PINNACLE consortium funded by the Wellcome Trust.

References:
[1] Wong, W.L., X. Su, X. Li, C.M. Cheung, R. Klein, C.Y. Cheng, and T.Y. Wong, Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta-analysis. The Lancet Global Health, 2014. 2(2): p. e106-e116.
[2] Sivaprasad, S., A. Bird, R. Nitiahpapand, L. Nicholson, P. Hykin, I. Chatziralli, et al., Perspectives on reticular pseudodrusen in age-related macular degeneration. Surv Ophthalmol, 2016. 61(5): p. 521-37.
[3] Khanifar, A.A., A.F. Koreishi, J.A. Izatt, and C.A. Toth, Drusen ultrastructure imaging with spectral domain optical coherence tomography in age-related macular degeneration. Ophthalmology, 2008. 115(11): p. 1883-90.