Abbasi Ashkan, Gowrisankaran Sowjanya, Lin Wei-Chun, Song Xubo, Antony Bhavna Josephine, Wollstein Gadi, Schuman Joel S, Ishikawa Hiroshi
Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon.
Ophthalmol Sci. 2025 Apr 24;5(5):100803. doi: 10.1016/j.xops.2025.100803. eCollection 2025 Sep-Oct.
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.
视野（VF）测试的纵向评估在青光眼管理中至关重要。传统的VF预测方法需要大量先前的测试，而深度学习技术在较少测试的情况下已显示出有前景的结果。本研究引入了一种混合深度学习框架，以提高VF测试预测的灵活性和准确性。
一项使用基于深度学习的VF预测模型的回顾性纵向研究。
共有1750名受试者（健康者和青光眼患者），收集了来自匹兹堡大学和纽约大学纵向青光眼队列的19437次Humphrey VF（24-2瑞典交互式阈值算法）测试数据。
训练了三种深度学习模型用于VF测试数据的逐点预测：（1）循环神经网络（RNN），（2）卷积神经网络（CNN）CascadeNet-5，以及（3）Hybrid-VF-Net，我们提出的将RNN与配备深度变换器的CNN相结合的方法，用于空间和时间建模。从多个角度分析结果，包括改变先前输入数据量的影响以及数据可靠性和疾病严重程度如何影响VF预测性能。
使用五折交叉验证评估预测的VF测试结果与实际结果之间的平均绝对误差。
我们发现特定的VF位置从局部或时间建模中受益更多，并且我们提出的方法使用混合策略优于比较方法。Hybrid-VF-Net对数据可靠性问题表现出更大的弹性，特别是在处理中重度青光眼病例中常见的高假阴性率时，这是由于复测变异性增加所致。此外，它在较少的先前VF测试下表现出改进的性能，从而减少了进展分析所需的等待时间。
所提出的Hybrid-VF-Net方法在性能和稳健性方面优于现有的深度学习VF方法。我们的研究结果突出了疾病严重程度、数据质量和时间偏移对预测性能的影响，某些VF位置从局部或时间建模中受益更多。中晚期青光眼病例数据的低可靠性仍然是一个挑战。因此，未来的研究可以改进时间建模并利用更大的数据集来进一步提高预测性能。
在本文末尾的脚注和披露中可能会发现专有或商业披露信息。
