Targeted spatial sampling using GOANNA improves detection of visual field progression.

Abstract

A new automated visual field testing approach that samples scotoma edges at a finer spatial resolution, GOANNA (Gradient-Oriented Automated Natural Neighbour Approach) was previously shown to improve accuracy and precision around those regions compared to current procedures in computer simulation. The purpose of this study was to observe if this improvement translated to more accurate classification of glaucomatous progression. Computer simulations were undertaken on six procedures: three variants of GOANNA on 150 locations; two variants of ZEST on 52 locations; and the ideal case where true thresholds are perfectly measured. The median number of presentations of GOANNA was matched to ZEST. The procedures were run on 156 sequences of simulated progressing fields and 156 sequences of stable fields to determine sensitivity and specificity using point-wise linear regression. Reliable (0% FP, 0% FN) and typical false positive (15% FP, 3% FN) response error conditions were investigated. Area under ROC curves (AUC) were plotted against the number of visual fields acquired to evaluate the performance of these procedures. The GOANNA framework exhibited equal or greater AUC than ZEST at all visits when baseline fields were initially defective (under both response error conditions) and when baseline fields were initially healthy when no false responses were made. Retest implementations of GOANNA exhibited an improvement over the original GOANNA after the first seven visits when fields were initially healthy. The results suggest that the improvement in precision and accuracy around scotoma borders seen in the GOANNA framework translates to earlier and more accurate detection of progressing fields compared with ZEST, especially in the early stages of glaucomatous progression.