Analysis of visual field progression in glaucoma.

Fitzke, Fred W.; Hitchings, Roger A.; Poinoosawmy, D; McNaught, Andrew; Crabb, David P.

doi:10.1136/bjo.80.1.40

Cited by 196 publications

(110 citation statements)

References 19 publications

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“…Ltd., Malmesbury, UK) que submete os campos consecutivos à análise de regressão linear ponto a ponto conforme detalhado anteriormente (14) . Pacientes estáveis ao longo do estudo foram definidos como os que: (a) persistiram mantendo escores AGIS iguais a zero e (b) cursaram sem demonstrar pontos progressores na análise de regressão linear Progressor ® (p<1%).…”

Section: Métodosunclassified

“…Todavia, a perimetria acromática automatizada é relativamente insensível para o diagnóstico precoce do glaucoma (2)(3)(4) e além disto é necessário realizar múltiplos exames para se averiguar se uma suspeita de alteração campimétrica é consistente (5)(6)(7)(8) . Alterações clínicas e histológicas na camada de fibras nervosas (CFN) da retina foram reportadas cerca de seis anos antes do aparecimento de alterações na perimetria acromática (9)(10) .…”

Section: Introductionunclassified

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Detecção de alterações na camada de fibras nervosas da retina por meio do laser confocal polarizado em hipertensão ocular antes do surgimento de defeitos perimétricos

Lauande-Pimentel

Barton

Poinoosawmy

et al. 2004

Arq. Bras. Oftalmol.

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Section: Métodosunclassified

Section: Introductionunclassified

Detecção de alterações na camada de fibras nervosas da retina por meio do laser confocal polarizado em hipertensão ocular antes do surgimento de defeitos perimétricos

Lauande-Pimentel

Barton

Poinoosawmy

et al. 2004

Arq. Bras. Oftalmol.

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“…Swift and Liu [26] have looked into learning statistical time series models of VF data. Other approaches that explore the temporal aspect of VF data include trend analysis [5,14], event analysis [14] and state space models [1]. Ibanez and Simo [17] have investigated spatio-temporal statistical models with the aim of forecasting visual field deterioration, but to date have only looked at visual fields of normal eyes.…”

Section: Introductionmentioning

confidence: 99%

A spatio-temporal Bayesian network classifier for understanding visual field deterioration

Tucker

Vinciotti

Liu

et al. 2005

Artificial Intelligence in Medicine

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Progressive loss of the field of vision is characteristic of a number of eye diseases such as glaucoma which is a leading cause of irreversible blindness in the world. Recently, there has been an explosion in the amount of data being stored on patients who suffer from visual deterioration including field test data, retinal image data and patient demographic data. However, there has been relatively little work in modelling the spatial and temporal relationships common to such data. In this paper we introduce a novel method for classifying Visual Field (VF) data that explicitly models these spatial and temporal relationships. We carry out an analysis of this method and compare it to a number of classifiers from the machine learning and statistical communities. Results are very encouraging showing that our classifiers are comparable to existing statistical models whilst also facilitating the understanding of underlying spatial and temporal relationships within VF data. The results reveal the potential of using such models for knowledge discovery within ophthalmic databases, such as networks reflecting the 'nasal step', an early indicator of the onset of glaucoma. The results outlined in this paper pave the way for a substantial program of study involving many other spatial and temporal datasets, including retinal image and clinical data.

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“…Analysis of visual field data has been used widely to predicting glaucoma deterioration; a variety of techniques including data classification, clustering [1] and statistical methods [2,3] have been used as a vehicle for this. At a more simplistic level, we know that the time of day when a test is applied can influence the value of a VF reading.…”

Section: Introductionmentioning

confidence: 99%

The AGIS Metric and Time of Test: A Replication Study

Counsell

Swift

Tucker

2016

2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)

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Abstract-Visual Field (VF) tests and corresponding data are commonly used in clinical practices to manage glaucoma. The standard metric used to measure glaucoma severity is the Advanced Glaucoma Intervention Studies (AGIS) metric. We know that time of day when VF tests are applied can influence a patient's AGIS metric value; a previous study showed that this was the case for a data set of 160 patients. In this paper, we replicate that study using data from 2468 patients obtained from Moorfields Eye Hospital. This may provide further evidence and support of this phenomenon in a replication sense. Results did indeed show a tendency for the metric to be lower for early onset patients in the morning; equally, for advanced patients, the effect was less pronounced. We thus found support for the earlier work of Montolio et al.[4] and add to the body of evidence on the AGIS metric.

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Analysis of visual field progression in glaucoma.

Cited by 196 publications

References 19 publications

Detecção de alterações na camada de fibras nervosas da retina por meio do laser confocal polarizado em hipertensão ocular antes do surgimento de defeitos perimétricos

Detecção de alterações na camada de fibras nervosas da retina por meio do laser confocal polarizado em hipertensão ocular antes do surgimento de defeitos perimétricos

A spatio-temporal Bayesian network classifier for understanding visual field deterioration

The AGIS Metric and Time of Test: A Replication Study

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