September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Introducing a novel technique to investigate performance of the GOANNA visual field algorithm in human observers
Author Affiliations & Notes
  • Luke Chong
    Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, Victoria, Australia
    School of Optometry, University of California Berkeley, Berkeley, California, United States
  • Andrew Turpin
    Department of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
  • Allison M McKendrick
    Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, Victoria, Australia
  • Footnotes
    Commercial Relationships   Luke Chong, None; Andrew Turpin, CenterVue SpA (F), Haag-Streit AG (F), Haag-Streit AG (R), Heidelberg Engineering GmBH (F); Allison McKendrick, CenterVue SpA (F), Haag-Streit AG (F), Haag-Streit AG (R), Heidelberg Engineering GmBH (F)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 3924. doi:
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    • Get Citation

      Luke Chong, Andrew Turpin, Allison M McKendrick; Introducing a novel technique to investigate performance of the GOANNA visual field algorithm in human observers. Invest. Ophthalmol. Vis. Sci. 2016;57(12):3924.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : We have developed a new perimetric algorithm called Gradient-Oriented Automated Natural Neighbor Approach (GOANNA). GOANNA does not test a fixed set of pre-determined locations, but instead customizes test location sampling without requiring prior knowledge of an individual’s visual field. Simulation studies predict GOANNA to display greater accuracy and precision than currently implemented perimetric procedures (Chong et al, IOVS, 2014). Here, we aimed to validate GOANNA in humans using a novel combination of computer simulation and human testing which we call Artificial Scotoma Generation.

Methods : Fifteen normal observers (mean age = 28 years, range = 21 to 45 years) participated. Baseline automated perimetry was performed on the Octopus 900. Visual field sensitivity was measured with two procedures: GOANNA and Zippy Estimation of Sequential Testing (ZEST). The selection of optimal parameters for GOANNA and ZEST were derived from previous computer simulation results. Four different scotoma types were induced in each observer by inserting a step between the algorithm and perimeter to alter presentation levels to simulate scotomata in human observers. Accuracy, precision and unique number of locations tested were recorded, with the maximum difference between a location and its neighbors (Max_d) used to stratify results. Locations which lie on a scotoma edge have a high Max_d, whereas locations within uniform areas of the visual field have a low Max_d.

Results : For all defect types, GOANNA sampled significantly more locations than ZEST (paired t-test, P < 0.001) whilst maintaining comparable test times. GOANNA displayed greater accuracy when Max_d was in the 10 to 30 dB range (with the exception of Max_d = 20 dB) (Wilcoxon, P < 0.001) and greater precision than ZEST when Max_d was in the 20 to 30 dB range (Wilcoxon, P < 0.001). For all other conditions, the accuracy and precision of ZEST and GOANNA were similar.

Conclusions : We have introduced a novel method for assessing accuracy of perimetric algorithms. Our results demonstrate greater accuracy and precision for GOANNA over ZEST, especially in regions surrounding scotoma edges. The results support those from earlier simulation studies, and provide justification for performing larger scale human trials with GOANNA in the future.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

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