Assessing the GOANNA Visual Field Algorithm Using Artificial Scotoma Generation on Human Observers - Chong, Luke X. (Transl Vis Sci Technol, 2016)
PURPOSE: To validate the performance of a new perimetric algorithm (Gradient-Oriented Automated Natural Neighbor Approach; GOANNA) in humans using a novel combination of computer simulation and human testing, which we call Artificial Scotoma Generation (ASG). METHODS: Fifteen healthy observers were recruited. Baseline conventional automated perimetry was performed on the Octopus 900. Visual field sensitivity was measured using two different procedures: GOANNA and Zippy Estimation by Sequential Testing (ZEST). Four different scotoma types were induced in each observer by implementing a novel technique that inserts a step between the algorithm and the perimeter, which in turn alters presentation levels to simulate scotomata in human observers. Accuracy, precision, and unique number of locations tested were measured, with the maximum difference between a location and its neighbors (Max_d) used to stratify results. RESULTS: GOANNA sampled significantly more locations than ZEST (paired t-test, P < 0.001), while maintaining comparable test times. Difference plots showed that GOANNA displayed greater accuracy than ZEST when Max_d was in the 10 to 30 dB range (with the exception of Max_d = 20 dB; Wilcoxon, P < 0.001). Similarly, GOANNA demonstrated greater precision than ZEST when Max_d was in the 20 to 30 dB range (Wilcoxon, P < 0.001). CONCLUSIONS: We have introduced a novel method for assessing accuracy of perimetric algorithms in human observers. Results observed in the current study agreed with the results seen in earlier simulation studies, and thus provide support for performing larger scale clinical trials with GOANNA in the future. TRANSLATIONAL RELEVANCE: The GOANNA perimetric testing algorithm offers a new paradigm for visual field testing where locations for testing are chosen that target scotoma borders. Further, the ASG methodology used in this paper to assess GOANNA shows promise as a hybrid between computer simulation and patient testing, which may allow more rapid development of new perimetric approaches.