A new look at threshold estimation algorithms for automated static perimetry

Automated perimetry is often associated with lengthy test times when a stai rcase algorithm is applied. This arises because the fixed step sizes used d uring threshold estimation (e.g.,4/2 dB) yield reduced test efficiency, wit h test times being dependent on the relative positioning of the start and e ndpoints, as well as the step size. Neighborhood logic may speed up the pro cess, although several presentations are still required for normal threshol d values and many more presentations are required for abnormal values. We c onsider whether there is any justification for using a fixed step size duri ng the threshold procedure. We show how empirical data can be applied, with in a Bayesian framework, to reduce test time with little or no loss of accu racy. Finally, we demonstrate the effect that the starting probability dens ity function can have on test efficiency by implementing an empirically det ermined and bimodal probability density function that provides fast outcome s.