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"New Insights Into Measurement Variability In Glaucomatous Visual Fields From Computer Modelling." and the language of the book is English.


“New Insights Into Measurement Variability In Glaucomatous Visual Fields From Computer Modelling.” Metadata:

  • Title: ➤  New Insights Into Measurement Variability In Glaucomatous Visual Fields From Computer Modelling.
  • Authors:
  • Language: English

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  • Internet Archive ID: pubmed-PMC3875443

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28PLoS%20ONE%29" rel="nofollow">PLoS ONE</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28PLoS%20ONE%29%20AND%20volume%3A%288%29" rel="nofollow">volume 8</a>.<h2>Abstract</h2>Objective: To develop a model to simulate visual fields (VFs) in glaucoma patients, and to characterize variability of the Mean Deviation (MD) VF summary measurement using real VFs and simulations. Methods: Pointwise VF variability was previously approximated using longitudinal VF data (24–2 SITA Standard, Humphrey Field Analyzer) from 2,736 patients; these data were used to build a non-parametric model to simulate VFs. One million VF simulations were generated from 1,000 VFs (1,000 simulations per ‘ground-truth’ VF), and the variability of simulated MDs was characterized as a function of ground-truth MD and Pattern Standard Deviation (PSD). Results: The median (interquartile range, IQR) patient age and MD was 66 (56 to 75) years and −3.5 (−8.3 to −1.1) decibels, respectively. The inferred variability as a function of ground-truth MD and PSD indicated that variability, on average, increased rapidly as glaucoma worsened. However, the pattern of VF damage significantly affects the level of MD variability, with more than three-fold differences between patients with approximately the same levels of MD but different patterns of loss. Conclusions: A novel approach for simulating VFs is introduced. A better understanding of VF variability will help clinicians to differentiate real VF progression from measurement variability. This study highlights that, overall, MD variability increases as the level of damage increases, but variability is highly dependent on the pattern of VF damage. Future research, using VF simulations, could be employed to provide benchmarks for measuring the performance of VF progression detection algorithms and developing new strategies for measuring VF progression.

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