  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.
