2019
DOI: 10.1080/01621459.2018.1537911
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Diagnosing Glaucoma Progression With Visual Field Data Using a Spatiotemporal Boundary Detection Method

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 und… Show more

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Cited by 11 publications
(6 citation statements)
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“…Such correlations have been investigated within the central 24° and 10° visual fields. 44 Asaoka reported that sectorization of 30-2 and 10-2 visual field locations were distinct and that the identified 10-2 sectors were stable on bootstrapping. 45 Such sectors tended to follow patterns of central RNFL bundles.…”
Section: Discussionmentioning
confidence: 99%
“…Such correlations have been investigated within the central 24° and 10° visual fields. 44 Asaoka reported that sectorization of 30-2 and 10-2 visual field locations were distinct and that the identified 10-2 sectors were stable on bootstrapping. 45 Such sectors tended to follow patterns of central RNFL bundles.…”
Section: Discussionmentioning
confidence: 99%
“…More details about the glaucoma population are given in Section 1 of the online Supplementary Materials. See Berchuck et al (2019b) for a more in depth introduction to visual fields for statisticians.…”
Section: Glaucoma Progression Using Visual Fieldsmentioning
confidence: 99%
“…Lee & Mitchell, 2012; Lu et al., 2007; Ma et al., 2010), with extensions to the spatiotemporal setting (e.g. Berchuck et al., 2019; Rushworth et al., 2017). Other methods have attempted to avoid some of the computing and identifiability issues suffered by these high‐dimensional models by viewing the entire neighbourhood structure as a single random quantity (Lee et al., 2014; Li et al., 2011).…”
Section: Introductionmentioning
confidence: 99%