Exploring Healthy Retinal Aging with Deep Learning.

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,

SUBMITTER: Menten MJ 

PROVIDER: S-EPMC10127123 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature