A deep learning model for generating fundus autofluorescence images from color fundus photography.

ABSTRACT:

Background

Fundus Autofluorescence (FAF) is a valuable imaging technique used to assess metabolic alterations in the retinal pigment epithelium (RPE) associated with various age-related and disease-related changes. The practical uses of FAF are ever-growing. This study aimed to evaluate the effectiveness of a generative deep learning (DL) model in translating color fundus (CF) images into synthetic FAF images and explore its potential for enhancing screening of age-related macular degeneration (AMD).

Methods

A generative adversarial network (GAN) model was trained on pairs of CF and FAF images to generate synthetic FAF images. The quality of synthesized FAF images was assessed objectively by common generation metrics. Additionally, the clinical effectiveness of the generated

SUBMITTER: Song F 

PROVIDER: S-EPMC10696390 | biostudies-literature | 2023 Nov-Dec

REPOSITORIES: biostudies-literature