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A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting
Journal article   Open access   Peer reviewed

A Hybrid Deep Learning–Based Approach for Visual Field Test Forecasting

Ashkan Abbasi, Sowjanya Gowrisankaran, Wei-Chun Lin, Xubo Song, Bhavna Josephine Antony, Gadi Wollstein, Joel S. Schuman and Hiroshi Ishikawa
Ophthalmology science (Online), v 5(5), 100803
01 Sep 2025
DOI: https://doi.org/10.1016/j.xops.2025.100803
PMID: 40520474
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.xops.2025.100803View
Published, Version of Record (VoR) Open

Abstract

Deep learning Glaucoma progression prediction Hybrid architecture Pointwise visual field forecasting Spatial and temporal modeling
Longitudinal assessment of visual field (VF) testing is essential in glaucoma management. Conventional VF forecasting methods require numerous prior tests, while deep learning techniques have shown promising results with fewer tests. This study introduces a hybrid deep learning framework to enhance flexibility and accuracy in VF test forecasting. A retrospective longitudinal study using deep learning–based VF forecasting models. A total of 1750 subjects (healthy and glaucoma patients) with 19 437 Humphrey VF (24-2 Swedish Interactive Threshold Algorithm) tests collected from longitudinal glaucoma cohorts at the University of Pittsburgh and New York University. Three deep learning models were trained for pointwise forecasting of VF test data: (1) a recurrent neural network (RNN), (2) CascadeNet-5, a convolutional neural network (CNN), and (3) Hybrid-VF-Net, our proposed method that combines an RNN with a CNN equipped with depthwise transformers for both spatial and temporal modeling. The results were analyzed from multiple perspectives, including the impact of varying the amount of prior input data and how data reliability and disease severity influence VF forecasting performance. Mean absolute error between predicted and actual VF test results was evaluated using five-fold cross-validation. We found that specific VF locations benefited more from either local or temporal modeling, and our proposed methods outperformed the compared approaches using a hybrid strategy. Hybrid-VF-Net exhibited greater resilience to data reliability issues, particularly in managing high false-negative rates often seen in moderate-to-severe glaucoma cases due to increased test–retest variability. Additionally, it demonstrated improved performance with fewer prior VF tests, thus reducing the waiting time needed for progression analysis. The proposed Hybrid-VF-Net method outperformed the existing deep learning VF methods in terms of performance and robustness. Our findings highlight the influence of disease severity, data quality, and time displacement on forecasting performance, with certain VF locations benefiting more from either local or temporal modeling. Low reliability in data from moderate to advanced glaucoma cases continues to pose a challenge. Therefore, future research could refine temporal modeling and leverage larger datasets to further enhance predictive performance. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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Domestic collaboration
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Web of Science research areas
Ophthalmology
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