A Hybrid Deep Learning-Based Approach for Visual Field Test Forecasting.

Objective: 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. Design: A retrospective longitudinal study using deep learning-based VF forecasting models. Subjects and Controls: 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. Methods: 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. Main Outcome Measures: Mean absolute error between predicted and actual VF test results was evaluated using five-fold cross-validation. Results: 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. Conclusions: 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. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.