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 (VFs) 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.

中文翻译：

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使用时空边界检测方法通过视野数据诊断青光眼进展


摘要 诊断青光眼进展对于限制不可逆的视力丧失至关重要。评估青光眼进展的常用方法是使用定期采集的一系列纵向视野 (VF)。由于数据生成过程和眼部解剖结构，VF 数据具有复杂的时空结构。因此，需要先进的统计方法来做出有关进展状态的临床确定。我们引入了一种时空边界检测模型，该模型允许视盘的底层解剖结构决定 VF 数据随时间变化的空间结构。我们表明，我们的新方法提供了对视力丧失的新颖见解，利用来自青光眼静脉搏动研究试验和狮子眼科研究所试验登记处的数据改善了青光眼进展的诊断。仿真结果表明，对于 VF 数据，所提出的方法优于现有的空间方法。本文的补充材料可在线获取，并且该方法在 R 包 womblR 中实现。