Deep-learning based phenotyping and prediction of disease progression in geographic atrophy secondaryto age-related macular degeneration

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Age-related macular degeneration (AMD) is the most common cause of central vision loss in developed countries today. For the non-neovascular late-stage manifestation, "geographical atrophy" (GA), no therapy has been established yet. As a prerequisite for clinical trials and personalized medicine we aim at (i.) an improved quantification of imaging biomarkers, (ii.) an unbiased, data-driven identification of phenotypes and (iii.) the prediction of functional loss. The planned analyses will be performed with state-of-the-art imaging data from the DFG-funded study Directional Spread in Geographic Atrophy. The Laboratory of Quantitative Imaging (PI: Prof. Daniel L. Rubin, Stanford University) will provide the expertise for the application of the latest deep learning based methods. The proposed project is divided into three work packages (WP):In WP1 we aim at deep-learning-based segmentation of GA and prediction of GA progression. Our goal is the fully automated identification and quantification of higher order structural features in optical coherence tomography (OCT), including differential drusen phenotypes, outer retinal tubulations (ORT) and Friedman-lipid globules, which have usually only been described qualitatively in the context of GA. This would enable a stratified selection of patients for future therapeutic trials.In WP2, we aim to establish an unbiased, data-driven approach to phenotype discovery based on high-resolution OCT image data using an auto-encoder architecture. Through systematic comparison with clinical data of monogenic diseases with known pathogenesis, we intend to identify discrete subtypes in the spectrum of AMD. Feature overlap with known monogenic diseases secondary to Bruch’s membrane alterations (Pseudoxanthoma elasticum, Sorsby Fundus Dystrophy, Late-onset retinal degeneration) and/or oxidative stress at the photoreceptor/pigment epithelium level (Stargardt disease, Central areolar choroidal dystrophy) could facilitate the development of future genotype-specific therapeutic approaches.In WP3, the goal is to predict the loss of vision due to GA over time, based on structural imaging data. The assessment of visual function in patients with GA tends to be time-consuming - especially fundus-controlled perimetry (FCP; also called "microperimetry"). The spatially resolved prediction and mapping of retinal function would enable the implementation of SD-OCT-based "quasi-functional" clinical study endpoints and facilitate clinical assessment with regard to the Driver's License Ordinance and clinical "low vision" rehabilitation.