Spatial Entropy Pursuit for Fast and Accurate Perimetry Testing.

Purpose: To propose a static automated perimetry strategy that increases the speed of visual field (VF) evaluation while retaining threshold estimate accuracy.

Methods: We propose a novel algorithm, spatial entropy pursuit (SEP), which evaluates individual locations by using zippy estimation by sequential testing (ZEST) but additionally uses neighboring locations to estimate the sensitivity of related locations. We model the VF with a conditional random field (CRF) where each node represents a location estimate that depends on itself as well as its neighbors. Tested locations are randomly selected from a pool of locations and new locations are added such that they maximally reduce the uncertainty over the entire VF. When no location can further reduce the uncertainty significantly, remaining locations are estimated from the CRF directly.

Results: SEP was evaluated and compared to tendency-oriented strategy, ZEST, and the Dynamic Test Strategy by using computer simulations on a test set of 245 healthy and 172 glaucomatous VFs. For glaucomatous VFs, root-mean-square error (RMSE) of SEP was comparable to that of existing strategies (3.4 dB), whereas the number of stimulus presentations of SEP was up to 23% lower than that of other methods. For healthy VFs, SEP had an RMSE comparable to evaluated methods (3.1 dB) but required 55% fewer stimulus presentations.

Conclusions: When compared to existing methods, SEP showed improved performances, especially with respect to test speed. Thus, it represents an interesting alternative to existing strategies.