Lotery, Andrew, Menten, Martin, Holland, Robbie, Leingang, Oliver, Bogunovic, Hrvoje, Hagag, Ahmed M., Kaye, Rebecca, Riedl, Sophie, Traber, Ghislaine, Hassan, osama, Pawlowski, Nick, Glocker, Ben, Fritsche, Lars G., Scholl, Hendrik P.N., Sivaprasad, Sobha, Schmidt-Erfurth, Ursula and Rueckert, Daniel (2023) Exploring healthy retinal aging with deep learning. Ophthalmology Science, 3 (3), [100294]. (doi:10.1016/j.xops.2023.100294).
Abstract
Purpose
To study the individual course of retinal changes caused by healthy aging using deep learning.
Design
Retrospective analysis of a large dataset of retinal optical coherence tomography (OCT) images.
Participants
Eighty-five thousand seven hundred and nine adults between the age of 40 and 75 years of whom OCT images were acquired in the scope of the UK Biobank population study.
Methods
We created a counterfactual generative adversarial network (GAN), a type of neural network, that learns from cross-sectional, retrospective data. It then synthesizes high-resolution counterfactual OCT images and longitudinal time series. These counterfactuals allow visualization and analysis of hypothetical scenarios in which certain characteristics of the imaged subject, such as age or sex, are altered while other attributes, crucially the subject’s identity and image acquisition settings, remain fixed.
Main Outcome Measures
Using our counterfactual GAN, we investigated subject-specific changes to the retinal layer structure as a function of age and sex. In particular, we measured changes to the retinal nerve fiber layer (RNFL), combined ganglion cell layer plus inner plexiform layer (GCIPL), inner nuclear layer to inner boundary of retinal pigment epithelium (INL-RPE) and retinal pigment epithelium (RPE).
Results
Our counterfactual GAN is able to smoothly visualize the individual course of retinal aging. Across all counterfactual images, the RNFL, GCIPL, INL-RPE and RPE changed by −0.1μm ± 0.1μm, −0.5μm ± 0.2μm, −0.2μm ± 0.1μm and 0.1μm ± 0.1μm, respectively, per decade of age. These results agree well with previous studies based on the same cohort from the UK Biobank population study. Beyond population-wide average measures, our counterfactual GAN allows us to explore whether the retinal layers of a given eye will increase in thickness, decrease in thickness or stagnate as a subject ages.
Conclusions
This study demonstrates how counterfactual GANs can aid research into retinal aging by generating high-resolution, high-fidelity OCT images and longitudinal time series. Ultimately, we envision that they will enable clinical experts to derive and explore hypotheses for potential imaging biomarkers for healthy and pathological aging that can be refined and tested in prospective clinical trials.
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