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Updated: Oct 24, 2026
Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
Published on: May 25, 2020
Longitudinal Pointwise Visual Field Prediction in Early-Stage Glaucoma Using Regression Models and Long Short-Term
Soheila Naderi1, Eghbal Mansoori2, Masoumeh Sadat Masoumpour3
1Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran, sums.ac.ir.
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.
Materials And Methods:
Data were simulated for patients with early-stage glaucoma using a linear mixed effects model (LMM) to account for noise in VF measurements and the impact of individual effects on glaucoma progression. Future VFs were predicted from examinations 6-11 based on previous examinations. Prediction accuracy was assessed using root mean squared error (RMSE) and pointwise mean absolute error (PMAE).
Results:
Evaluation of the four approaches showed that OLR and naive BL forecast achieved higher errors than AR(1) and LSTM models. LSTM performed best when the number of examinations was small (in the initial examinations, indeed) in slow, moderate, and fast progression rate scenarios. However, with a larger number of examinations, prediction errors increased for LSTM compared with AR(1), resulting in higher accuracy for AR(1), particularly under moderate and fast progression rate scenarios.
Conclusion:
The performance of the models shows dependence on the number of examinations in the present study. These findings highlight the importance of temporal information in longitudinal VF prediction.
