Diagnosing Glaucoma Progression with Visual Field Data Using a Spatiotemporal Boundary Detection Method

TL;DR

This paper introduces a novel spatiotemporal boundary detection model that leverages ocular anatomy to improve the diagnosis of glaucoma progression from visual field data, outperforming existing methods.

Contribution

The paper presents a new statistical model that incorporates anatomical information for better analysis of visual field data in glaucoma progression detection.

Findings

The method improves diagnosis accuracy over existing spatial models.

Simulations demonstrate the model's preference and robustness.

Application to clinical data shows practical utility.

Abstract

Diagnosing glaucoma progression is critical for limiting irreversible vision loss. A common method for assessing glaucoma progression uses a longitudinal series of visual fields (VF) acquired at regular intervals. VF data are characterized by a complex spatiotemporal structure due to the data generating process and ocular anatomy. Thus, advanced statistical methods are needed to make clinical determinations regarding progression status. We introduce a spatiotemporal boundary detection model that allows the underlying anatomy of the optic disc to dictate the spatial structure of the VF data across time. We show that our new method provides novel insight into vision loss that improves diagnosis of glaucoma progression using data from the Vein Pulsation Study Trial in Glaucoma and the Lions Eye Institute trial registry. Simulations are presented, showing the proposed methodology is preferred over existing spatial methods for VF data. Supplementary materials for this article are available online and the method is implemented in the R package womblR.