Exploring Healthy Retinal Aging with Deep Learning.
Menten Martin J, Holland Robbie, Leingang Oliver, Bogunović Hrvoje, Hagag Ahmed M, Kaye Rebecca, Riedl Sophie, Traber Ghislaine L, Hassan Osama N, Pawlowski Nick
AI Summary
Deep learning visualized individual retinal layer changes with healthy aging, showing specific layer thinning/thickening. This tool can help identify biomarkers for healthy and pathological aging.
Abstract
Purpose
To study the individual course of retinal changes caused by healthy aging using deep learning.
Design
Retrospective analysis of a large data set of retinal OCT images.
Participants
A total of 85 709 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, whereas 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 in the retinal layer structure as a function of age and sex. In particular, we measured changes in the retinal nerve fiber layer (RNFL), combined ganglion cell layer plus inner plexiform layer (GCIPL), inner nuclear layer to the inner boundary of the 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 -0.1 μm ± 0.1 μm, -0.5 μm ± 0.2 μm, -0.2 μm ± 0.1 μm, and 0.1 μm ± 0.1 μm, 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.
Conclusion
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 pathologic aging that can be refined and tested in prospective clinical trials.
Financial disclosures: Proprietary or commercial disclosure may be found after the references.
Shields Classification
Key Concepts6
Across all counterfactual images generated by the GAN, the retinal nerve fiber layer (RNFL) changed by -0.1 μm ± 0.1 μm per decade of age in healthy aging.
Across all counterfactual images generated by the GAN, the combined ganglion cell layer plus inner plexiform layer (GCIPL) changed by -0.5 μm ± 0.2 μm per decade of age in healthy aging.
Across all counterfactual images generated by the GAN, the inner nuclear layer to the inner boundary of the retinal pigment epithelium (INL-RPE) changed by -0.2 μm ± 0.1 μm per decade of age in healthy aging.
Across all counterfactual images generated by the GAN, the retinal pigment epithelium (RPE) changed by 0.1 μm ± 0.1 μm per decade of age in healthy aging.
A counterfactual generative adversarial network (GAN) was created to study the individual course of retinal changes caused by healthy aging using deep learning, learning from cross-sectional, retrospective data to synthesize high-resolution counterfactual OCT images and longitudinal time series.
The counterfactual GAN, applied to a dataset of 85,709 adults aged 40 to 75 years from the UK Biobank population study, was able to smoothly visualize the individual course of retinal aging.
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