Conditional GAN for Prediction of Glaucoma Progression with Macular Optical Coherence Tomography

TL;DR

This paper introduces a conditional GAN model that predicts future OCT scans for glaucoma patients, potentially enabling earlier detection of disease progression using minimal prior data.

Contribution

The study develops a novel conditional GAN approach to forecast glaucoma-related OCT images, demonstrating high accuracy with limited prior measurements.

Findings

Predicted OCT scans closely match actual images.

Two prior visits may suffice to predict six-month progression.

The model offers a non-invasive tool for early glaucoma monitoring.

Abstract

The estimation of glaucoma progression is a challenging task as the rate of disease progression varies among individuals in addition to other factors such as measurement variability and the lack of standardization in defining progression. Structural tests, such as thickness measurements of the retinal nerve fiber layer or the macula with optical coherence tomography (OCT), are able to detect anatomical changes in glaucomatous eyes. Such changes may be observed before any functional damage. In this work, we built a generative deep learning model using the conditional GAN architecture to predict glaucoma progression over time. The patient's OCT scan is predicted from three or two prior measurements. The predicted images demonstrate high similarity with the ground truth images. In addition, our results suggest that OCT scans obtained from only two prior visits may actually be sufficient to predict the next OCT scan of the patient after six months.