A J Vingrys1, M J Pianta. 1. Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia. a.vingrys@optometry.unimelb.edu.au
Abstract
BACKGROUND: Automated perimetry is associated with lengthy test times, but Baysean predictions can be applied to speed up testing. A critical component of such methods is the starting probability density function (PDF). METHODS/ RESULTS: In the present study we show that a unimodal PDF, suggested n the literature as adequate for clinical data, fails to describe the thresholds of diseased eyes and we develop a bi-modal PDF representative of a clinical population. CONCLUSION: We suggest that the implementation of a bi-modal PDF will save test time and retain test accuracy.
BACKGROUND: Automated perimetry is associated with lengthy test times, but Baysean predictions can be applied to speed up testing. A critical component of such methods is the starting probability density function (PDF). METHODS/ RESULTS: In the present study we show that a unimodal PDF, suggested n the literature as adequate for clinical data, fails to describe the thresholds of diseased eyes and we develop a bi-modal PDF representative of a clinical population. CONCLUSION: We suggest that the implementation of a bi-modal PDF will save test time and retain test accuracy.
Authors: Andrew John Anderson; Chris A Johnson; Murray Fingeret; John L Keltner; Paul G D Spry; Michael Wall; John S Werner Journal: Invest Ophthalmol Vis Sci Date: 2005-04 Impact factor: 4.799
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