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Comparing the Use of Measured and Smoothed Data in Forecasting Visual Field Tests Using Deep Learning.

Ashkan Abbasi¹, Sowjanya Gowrisankaran¹, Wei-Chun Lin¹

¹Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.

Ophthalmology Science

|June 19, 2026

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This summary is machine-generated.

Training deep learning models with smoothed visual field (VF) data improves forecasting accuracy by focusing on long-term trends. This approach enhances the reliability of VF forecasting for glaucoma patients.

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Visual field (VF) tests are crucial for diagnosing and monitoring glaucoma.
Forecasting VF changes accurately is essential for timely intervention.
Deep learning (DL) models show promise for VF forecasting but can be affected by test variability.

Purpose of the Study:

To evaluate the impact of using smoothed visual field (VF) targets versus measured VF targets for training and testing DL models.
To assess the forecasting accuracy of DL models under different training and testing configurations.

Main Methods:

Retrospective analysis of 19,437 Humphrey VF tests from 1400 subjects (healthy and glaucoma patients).
Three DL-based pointwise VF forecasting methods were trained and tested using measured VF targets and smoothed VF targets (constructed via linear regression).

Keywords:

Deep learning Glaucoma Long-term visual field test forecasting Visual field testing

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Models were evaluated using fivefold cross-validation and mean absolute error (MAE).

Main Results:

Models trained and tested on smoothed VF targets consistently achieved lower mean absolute errors (MAEs) compared to those trained on measured VF targets.
Performance improvements were most significant in the 0.5- to 1.5-year forecast range.
Models trained with smoothed targets demonstrated comparable performance when evaluated against measured VF targets.

Conclusions:

Using smoothed VF targets for training DL models enhances forecasting accuracy by enabling the models to learn long-term trends.
This method mitigates the impact of noise and short-term variability inherent in VF test data.
The approach of using smoothed targets is recommended for future DL-based VF modeling efforts to better reflect clinically meaningful functional changes.