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Related Experiment Video

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Exploring Healthy Retinal Aging with Deep Learning.

Martin J Menten1,2, Robbie Holland1, Oliver Leingang3

  • 1BioMedIA, Imperial College London, London, United Kingdom.

Ophthalmology Science
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

Deep learning models visualize individual retinal aging. Counterfactual generative adversarial networks (GANs) reveal subject-specific changes in retinal layers, aiding biomarker discovery for healthy and pathologic aging.

Keywords:
AgingBiomarker discoveryDeep learningMachine learningRetina

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Healthy aging causes changes in retinal layer structure.
  • Understanding individual aging trajectories is crucial for identifying potential biomarkers.
  • Previous studies analyzed population-wide changes in retinal layers.

Purpose of the Study:

  • To investigate the individual course of retinal changes during healthy aging using deep learning.
  • To develop a method for visualizing hypothetical scenarios of retinal aging.
  • To analyze subject-specific alterations in retinal layers with age and sex.

Main Methods:

  • Retrospective analysis of 85,709 retinal OCT images from the UK Biobank.
  • Development of a counterfactual generative adversarial network (GAN) to synthesize high-resolution OCT images and longitudinal time series.
  • Generation of counterfactual images by altering age/sex while keeping subject identity fixed.

Main Results:

  • The counterfactual GAN successfully visualized individual retinal aging trajectories.
  • Quantified average age-related changes per decade: RNFL -0.1 μm, GCIPL -0.5 μm, INL-RPE -0.2 μm, RPE +0.1 μm.
  • Demonstrated the ability to explore individual variations in retinal layer thickness changes (increase, decrease, or stagnation).

Conclusions:

  • Counterfactual GANs are effective tools for researching retinal aging.
  • This approach generates high-fidelity OCT images and longitudinal data for hypothesis generation.
  • Enables exploration of potential imaging biomarkers for healthy and pathological aging, guiding future clinical trials.