Developing a clinical probability density function for automated perimetry.
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).


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.


CONCLUSIONS
We suggest that the implementation of a bi-modal PDF will save test time and retain test accuracy.
Department of Optometry and Vision Sciences, University of Melbourne, Parkville, Victoria, Australia. a.vingrys@optometry.unimelb.edu.au
A J
AJ
Vingrys
M J
MJ
Pianta
D000328:Adult; D005128:Eye Diseases / Q000175:diagnosis*; D006801:Humans; D008875:Middle Aged; D011336:Probability; D012189:Retrospective Studies; D058609:Visual Field Tests / Q000379:methods*; D014794:Visual Fields