Incorporating Spatial Models in Visual Field Test Procedures

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 glaucomatous and 233 normal VFs (Humphrey Field Analyzer 24-2). Output measures included number of presentations and visual sensitivity estimates. RESULTS: There was no significant difference in accuracy or precision of SWeLZ for the different spatial models relative to ZEST, either when collated across whole fields or when split by input sensitivity. Inspection of VF maps showed that SWeLZ was able to detect localized VF loss. SWeLZ was faster than ZEST for normal VFs: median number of presentations reduced by 20% to 38%. The number of presentations was equivalent for SWeLZ and ZEST when simulated on glaucomatous VFs. CONCLUSIONS: SWeLZ has the potential to reduce VF test times in people with normal VFs, without detriment to output precision and accuracy in glaucomatous VFs. TRANSLATIONAL RELEVANCE: SWeLZ is a novel perimetric algorithm. Simulations show that SWeLZ can reduce the number of test presentations for people with normal VFs. Since many patients have normal fields, this has the potential for significant time savings in clinical settings.