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

doi:10.1016/j.xops.2025.100803

2025-04-24

Ashkan Abbasi

Sowjanya Gowrisankaran

DOI：10.1016/j.xops.2025.100803

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摘要

En 中文

视敏度（VF）测试的纵向评估对于青光眼管理至关重要。传统的VF预测方法需要大量先前的测试，而深度学习技术已在较少测试的情况下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，以增强VF测试预测的灵活性和准确性。

Keyword:

Pointwise visual field forecasting

Glaucoma progression prediction

Deep learning

Hybrid architecture

Spatial and temporal modeling

CNN

convolutional neural network

decibels

fixation loss

FNR

false-negative rate

MAE

mean absolute error

mean deviation

RNN

recurrent neural network

visual field

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Ophthalmology Science

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