Journal article
Incorporating spatial models in visual field test procedures
NJ Rubinstein, AM McKendrick, A Turpin
Translational Vision Science and Technology | Published : 2016
DOI: 10.1167/tvst.5.2.7
Abstract
Purpose: To introduce a perimetric algorithm (Spatially Weighted Likelihoods in Zippy Estimation by Sequential Testing [ZEST] [SWeLZ]) that uses spatial information on every presentation to alter visual field (VF) estimates, to reduce test times without affecting output precision and accuracy. Methods: SWeLZ is a maximum likelihood Bayesian procedure, which updates probability mass functions at VF locations using a spatial model. Spatial models were created from empirical data, computational models, nearest neighbor, random relationships, and interconnecting all locations. SWeLZ was compared to an implementation of the ZEST algorithm for perimetry using computer simulations on 163 glaucomato..
View full abstractGrants
Awarded by ARC
Awarded by Victorian Life Sciences Computation Initiative (VLSCI) grant on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia
Awarded by Australian Research Council
Funding Acknowledgements
This research was supported by grant ARC LP130100055 (AT and AMM) and a Victorian Life Sciences Computation Initiative (VLSCI) grant [VR0280] on its Peak Computing Facility at the University of Melbourne, an initiative of the Victorian Government, Australia.