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

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.

诊断青光眼进展对于遏制不可逆性视力丧失至关重要。评估青光眼进展的常用方法，是采用定期采集的纵向视野（visual fields, VF）序列。由于数据生成过程与眼部解剖结构的影响，视野数据具有复杂的时空结构特征，因此需借助先进的统计方法来完成青光眼进展状态的临床判定。本研究提出一种时空边界检测模型，该模型可基于视盘的潜在解剖结构，决定视野数据随时间变化的空间结构。本研究表明，借助青光眼静脉搏动研究试验（Vein Pulsation Study Trial in Glaucoma）与狮子眼研究所（Lions Eye Institute）试验注册库的数据，所提新方法可对视力丧失情况提供全新见解，从而优化青光眼进展的诊断效果。仿真实验结果显示，针对视野数据，所提方法的性能优于现有空间分析方法。本文的补充材料可在线获取，且该方法已在R语言包womblR中实现。