# Improving the robustness of the Sequentially Optimized Reconstruction Strategy (SORS) for visual field testing

## TL;DR

This paper introduces a new method for visual field testing that uses dimensionality reduction to improve accuracy and efficiency.

## Contribution

The novel TTPCR method uses principal component analysis to enhance reconstruction robustness in visual field testing.

## Key findings

- TTPCR performs as well as original methods with large datasets.
- TTPCR requires 22% fewer trials to achieve the same error with small datasets.
- Dimensionality reduction improves robustness in visual field reconstruction.

## Abstract

Perimetry, or visual field test, estimates differential light sensitivity thresholds across many locations in the visual field (e.g., 54 locations in the 24–2 grid). Recent developments have shown that an entire visual field may be relatively accurately reconstructed from measurements of a subset of these locations using a linear regression model. Here, we show that incorporating a dimensionality reduction layer can improve the robustness of this reconstruction. Specifically, we propose to use principal component analysis to transform the training dataset to a lower dimensional representation and then use this representation to reconstruct the visual field. We named our new reconstruction method the transformed-target principal component regression (TTPCR). When trained on a large dataset, our new method yielded results comparable with the original linear regression method, demonstrating that there is no underfitting associated with parameter reduction. However, when trained on a small dataset, our new method used on average 22% fewer trials to reach the same error. Our results suggest that dimensionality reduction techniques can improve the robustness of visual field testing reconstruction algorithms.