Longitudinal Pointwise Visual Field Prediction in Early-Stage Glaucoma Using Regression Models and Long Short-Term Memory: A Simulation Study

Background

Glaucoma is a progressive optic neuropathy in which longitudinal visual field (VF) prediction is essential for timely clinical decision-making and preventing irreversible VF loss. However, accurate prediction remains challenging due to the noisy, high-dimensional nature of VF data and their complex longitudinal patterns. Capturing these patterns may require increasingly sophisticated prediction models, but greater model complexity can reduce interpretability. Therefore, prediction models need to capture temporal patterns while balancing interpretability and computational feasibility. This study is aimed at evaluating and comparing the performance of four methods, including personalized models (first-order autoregressive [AR(1)] and ordinary linear regression (OLR)), a population-level long short-term memory (LSTM), and a naive baseline (BL) forecast for glaucoma VF prediction.