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

IF 3.2 Q1 OPHTHALMOLOGY
Ashkan Abbasi PhD , Sowjanya Gowrisankaran PhD , Wei-Chun Lin MD, PhD , Xubo Song PhD , Bhavna Josephine Antony PhD , Gadi Wollstein MD , Joel S. Schuman MD , Hiroshi Ishikawa MD
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引用次数: 0

Abstract

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 Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
基于混合深度学习的视野测试预测方法
目的对青光眼患者进行纵向视野检查是青光眼治疗的重要手段。传统的VF预测方法需要大量的预先测试,而深度学习技术在较少的测试中显示出有希望的结果。本研究引入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。设计一项基于深度学习的VF预测模型的回顾性纵向研究。受试者和对照组:从匹兹堡大学和纽约大学青光眼纵向队列中收集19437例Humphrey VF(24-2瑞典交互式阈值算法)测试,共1750名受试者(健康和青光眼患者)。方法对三个深度学习模型进行VF测试数据的点对预测:(1)递归神经网络(RNN), (2) CascadeNet-5,卷积神经网络(CNN),以及(3)Hybrid-VF-Net,我们提出的方法将RNN与配备深度变压器的CNN结合起来进行时空建模。结果从多个角度进行分析,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。预测和实际VF测试结果之间的平均绝对误差采用五倍交叉验证进行评估。结果我们发现特定的VF位置从局部或时间建模中获益更多,并且我们提出的方法优于使用混合策略的比较方法。Hybrid-VF-Net在数据可靠性问题上表现出更强的弹性,特别是在处理中重度青光眼病例中常见的高假阴性率时,由于测试-重测试变异性的增加。此外,它通过更少的先前VF测试证明了性能的提高,从而减少了进度分析所需的等待时间。结论Hybrid-VF-Net方法在性能和鲁棒性方面优于现有的深度学习VF方法。我们的研究结果强调了疾病严重程度、数据质量和时间位移对预测性能的影响,某些VF位置从局部或时间建模中获益更多。中度至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。财务披露专有或商业披露可在本文末尾的脚注和披露中找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Ophthalmology science
Ophthalmology science Ophthalmology
CiteScore
3.40
自引率
0.00%
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0
审稿时长
89 days
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