Glaucoma, a leading cause of irreversible blindness, disproportionately affects veterans. While often progressing slowly, glaucoma can also progress rapidly, and especially given the variability of standard visual-field (VF) tests to monitor progression, it currently can be challenging to determine those individuals needing a more aggressive treatment plan. Veterans may experience permanent loss of vision (and corresponding vision-related quality of life) while waiting for subsequent tests to show VF loss progression (and thus indicating a change in treatment is needed). Structural optical coherence tomography (OCT) measures, such as the thickness of the macular ganglion cell layer (GCL), retinal nerve fiber layer (RNFL) and optic disc morphology can also be used to help monitor progression. However, existing clinical use of global parameters to assess glaucoma progression may be insensitive to worsening of focal defects. It is also not known how differing spatial patterns of progression affects quality of life. There is an unmet clinical need for simple-to-use approaches to more accurately estimate future progression and corresponding quality-of-life measures. We will use a specific type of deep-learning approach, called deep variational autoencoders (VAEs) to provide a novel standardized and sensitive approach to monitoring glaucomatous progression, comparable to a glaucoma expert. Our specific aims are as follows: 1. Evaluate how well image-based deep-learning variational autoencoder (VAE) models can be used to monitor a patient’s current glaucomatous progression. This aim will first involve training and evaluating a separate deep VAE model for each image-based structure of interest as well as a deep VAE model for 24-2 visual field threshold data. Once trained, each VAE model will allow for the extraction of the so-called latent variable values given the input image. The ability of these latent variable values to monitor change over time will be compared (in an independent test set) to standard global and regional parameters. Because of their ability to naturally capture both global and local changes, the latent-variable approach will be able to better detect changes over time compared to current clinical reports. 2. Evaluate how well image-based deep-learning variational autoencoder (VAE) models can be used to predict a patient’s future glaucomatous progression. In this aim, we will first develop an approach for predicting future latent-variable representations of structure/function based on learning from a prior time series of values. Once determined, future latent values will be mapped back to their original structure/function representations using the trained “decoder” part of the VAE. Such an approach will provide a clear advantage for a clinician in having visual spatial representations of future structure and function trajectories to optimize early treatment decisions. 3. Evaluate how latent variables from a novel binocula...