Document Type

Article

Publication Date

5-12-2026

Comments

This article is the author’s final published version in Ophthalmology Science, Volume 6, Issue 7, 2026, Article number 101234.

The published version is available at https://doi.org/10.1016/j.xops.2026.101234. Copyright © 2026 American Academy of Ophthalmology, Inc.

 

Abstract

OBJECTIVE: To evaluate the impact of training and testing deep learning (DL) models for visual field (VF) forecasting using input-target pairs in which the target is either the measured VF test result, or its smoothed counterpart constructed via linear regression.

DESIGN: A retrospective data analysis study evaluating DL models for VF forecasting under multiple training and testing configurations.

SUBJECTS AND CONTROLS: A total of 1400 subjects (healthy and glaucoma patients) with 19 437 reliable Humphrey VF (24-2 Swedish Interactive Threshold Algorithm) tests collected from longitudinal cohorts at the University of Pittsburgh and New York University.

METHODS: Three DL-based pointwise VF forecasting methods were trained and tested under 4 different configurations formed by using measured and smoothed VF targets. Smoothed targets were constructed by applying linear regression over triplets of consecutive VF tests. Models were assessed using fivefold cross-validation and mean absolute error (MAE) as the training and testing metric.

MAIN OUTCOME MEASURES: Mean absolute error of forecasted VF test results under various training and testing configurations.

RESULTS: Models trained and tested on smoothed VF targets consistently achieved lower MAEs compared to those trained and tested on measured VF targets. The performance improvements were most prominent in the 0.5- to 1.5-year forecast range. Furthermore, models trained with smoothed VF targets showed comparable performance when evaluated against measured VF targets.

CONCLUSIONS: Using smoothed VF targets for training improves forecasting accuracy by guiding DL models to learn long-term trends in the data rather than forcing them to model noise and short-term variabilities, which are prevalent in VF test data. This approach aligns with clinical goals of assessing meaningful functional changes over time and suggested to be considered in future DL-based VF modeling efforts.

FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

PubMed ID

42317776

Language

English

Download

Share

COinS