Customized, Automated Stimulus Location Choice for Assessment of Visual Field Defects

PURPOSE. To introduce a perimetric algorithm (gradient-oriented automated natural neighbor approach [GOANNA]) that automatically chooses spatial test locations to improve characterization of visual field (VF) loss without increasing test times. METHODS. Computer simulations were undertaken to assess the performance of GOANNA. GOANNA was run on a 3 degrees grid of 150 locations, and was compared with a zippy estimation by sequential testing (ZEST) thresholding strategy for locations in the 24-2 test pattern, with the remaining 98 locations being interpolated. Simulations were seeded using empirical data from 23 eyes with glaucoma that were measured at all 150 locations. The performance of the procedures was assessed by comparing the output thresholds to the input thresholds (accuracy and precision) and by evaluating the number of presentations required for the procedure to terminate (efficiency). RESULTS. When collated across whole-fields, there was no significant difference in accuracy, precision, or efficiency between GOANNA and ZEST. However, GOANNA targeted presentations on scotoma borders; hence it was more precise and accurate at locations where the sensitivity gradient within the VF was high. CONCLUSIONS. Compared with ZEST, GOANNA was marginally less precise in areas of the VF that had spatially uniform sensitivity, but improved accuracy and precision in regions surrounding scotoma edges. GOANNA provides a principled framework for automatic placement of additional test locations to provide spatially denser testing around the borders of VF loss.