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Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning
Journal article   Open access   Peer reviewed

Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning

Ashkan Abbasi, Sowjanya Gowrisankaran, Wei-Chun Lin, Xubo Song, Bhavna Josephine Antony, Gadi Wollstein, Joel S. Schuman and Hiroshi Ishikawa
Ophthalmology science (Online), v 6(7), 101234
01 Jul 2026
DOI: https://doi.org/10.1016/j.xops.2026.101234
PMID: 42317776
Featured in Collection :   Drexel's Newest Publications
url
https://doi.org/10.1016/j.xops.2026.101234View
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Abstract

Deep learning Glaucoma Long-term visual field test forecasting Visual field testing
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. A retrospective data analysis study evaluating DL models for VF forecasting under multiple training and testing configurations. 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. 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. Mean absolute error of forecasted VF test results under various training and testing configurations. 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. 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. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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