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Assessing the GOANNA Visual Field Algorithm Using Artificial Scotoma Generation on Human Observers.

Luke X Chong1, Andrew Turpin2, Allison M McKendrick3

  • 1Department of Optometry & Vision Sciences The University of Melbourne, Melbourne, Australia ; School of Optometry, University of California Berkeley, Berkeley, CA, USA.

Translational Vision Science & Technology
|September 14, 2016
PubMed
Summary

The Gradient-Oriented Automated Natural Neighbor Approach (GOANNA) algorithm shows improved accuracy and precision in visual field testing compared to ZEST. This new method, Artificial Scotoma Generation (ASG), validates GOANNA

Keywords:
GOANNAalgorithmsperimetrypsychophysicsvisual fields

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Area of Science:

  • Ophthalmology
  • Visual field testing
  • Computational vision

Background:

  • Automated perimetry is crucial for diagnosing and monitoring visual field defects.
  • Existing algorithms require validation in human subjects to ensure clinical applicability.
  • Novel methods are needed to efficiently assess the performance of new perimetric algorithms.

Purpose of the Study:

  • To validate the Gradient-Oriented Automated Natural Neighbor Approach (GOANNA) perimetric algorithm in human observers.
  • To introduce and utilize Artificial Scotoma Generation (ASG) as a novel method combining computer simulation and human testing for algorithm validation.

Main Methods:

  • Fifteen healthy observers underwent visual field sensitivity testing using GOANNA and Zippy Estimation by Sequential Testing (ZEST).
  • Artificial Scotoma Generation (ASG) was employed to induce four types of scotoma by altering presentation levels.
  • Accuracy, precision, and the number of tested locations were measured, stratified by the maximum difference between neighboring locations (Max_d).

Main Results:

  • GOANNA tested significantly more locations than ZEST in comparable test times.
  • GOANNA demonstrated superior accuracy and precision compared to ZEST, particularly for scotoma borders within the 10-30 dB range of Max_d.
  • Results align with previous simulation studies, supporting GOANNA's potential clinical utility.

Conclusions:

  • The study introduces a novel human-based validation method (ASG) for perimetric algorithms.
  • GOANNA proves to be a promising perimetric algorithm with enhanced accuracy and precision.
  • The ASG methodology offers a hybrid approach for rapid development of new perimetric techniques.