Generative Artificial Intelligence for Retinal Image Translation to Improve Glaucoma Screening With Deep Learning.

ABSTRACT:

Purpose

The purpose of this study was to improve automated glaucoma detection by utilizing generative adversarial networks (GANs) to translate underutilized scanning laser ophthalmoscopy (SLO) fundus images into synthetic color fundus (CF) photographs.

Methods

A Cycle-Consistent GAN model (CycleGAN) framework was used to translate 16,936 SLO fundus photographs into corresponding synthetic CF images. Five deep learning models were trained using real CF, synthetic CF, SLO fundus, and combined datasets to classify glaucoma from a holdout test set of real CF photographs. Model performance was evaluated using the area under the operating characteristic curve (AUC) and sensitivity at 90% and 95% specificities.

Results

The "GAN+CFP" model, trained on real and synthetic CF im

SUBMITTER: Majid I 

PROVIDER: S-EPMC12663884 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature