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Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.

Abstract：

PURPOSE:To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields. METHODS:In an earlier study, it was shown that a model using vB-ICA-mm can separate normal fields from fields with six different patterns of visual field loss related to glaucomatous optic neuropathy (GON) along maximally independent axes. In the present study, an independent group of 191 patient eyes (66 with ocular hypertension (OHT), 12 with suspected glaucoma by field, 61 with suspected glaucoma by disc, and 52 with glaucoma) with five or more standard visual fields under observation for a mean of 6.24 +/- 2.65 years and 8.11 +/- 2.42 visual fields were evaluated with the vB-ICA-mm. In addition, eyes with progressive GON (PGON) were identified (n = 39). Each participant had a series of fields tested, with each field entered independently and placed along the axes of the previously developed model. This allowed change in one pattern of visual field defect (along one axis) to be assessed relative to results other areas of that same field (no change along other axes). Progression was based on a slope falling outside the 5th and the 95th percentile limits of all slopes, with at least two axes not showing such a deviation in a given individual's series of fields. Fields were also scored using Advanced Glaucoma Intervention Study (AGIS) and the Early Manifest Glaucoma Treatment Trial (EMGT) criteria. RESULTS:Thirty-two of 191 eyes progressed on vB-ICA-mm by this definition. Of the 32, 22 had field loss at baseline, 7 had only GON, 3 were OHTs and 12 were from the 39 eyes (31%) with PGON. The vB-ICA-mm identified a higher percentage of progressing eyes in each diagnostic category than did AGIS or and the EMGT. CONCLUSIONS:The vB-ICA-mm can quantitatively identify progression in eyes with glaucoma by evaluating change in one or more patterns of the visual field loss while other areas or patterns remain stable. This may enable each eye to contribute to the determination of whether change is caused by true progression or by variability.

journal_name

Invest Ophthalmol Vis Sci

journal_title

Investigative ophthalmology & visual science

authors

Sample PA,Boden C,Zhang Z,Pascual J,Lee TW,Zangwill LM,Weinreb RN,Crowston JG,Hoffmann EM,Medeiros FA,Sejnowski T,Goldbaum M

doi

10.1167/iovs.04-1168

keywords:

subject

Has Abstract

pub_date

2005-10-01 00:00:00

pages

3684-92

issue

eissn

0146-0404

issn

1552-5783

pii

46/10/3684

journal_volume

pub_type

杂志文章

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Unsupervised machine learning with independent component analysis to identify areas of progression in glaucomatous visual fields.