研究目的
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).
研究成果
The study introduced a novel method for assessing the accuracy of perimetric algorithms in human observers, validating the performance of GOANNA. Results supported the potential for larger scale clinical trials with GOANNA in the future, highlighting its advantages in targeting scotoma borders and offering a new paradigm for visual field testing.
研究不足
The methodology of artificially inducing scotomata in healthy observers enabled estimation of accuracy but did not completely mimic the behavior of people with disease due to the likely flattening of the frequency-of-seeing slope in those people. Additionally, eye movement was not monitored with gaze or fundus tracking, which could influence test–retest variability and the slope of scotoma edges.
1：Experimental Design and Method Selection:
The study involved the implementation of the GOANNA and ZEST procedures on an Octopus 900 perimeter to measure visual field sensitivity. A novel technique, ASG, was used to induce scotomata in healthy observers.
2：Sample Selection and Data Sources:
Fifteen healthy observers were recruited, with specific inclusion criteria to ensure normal visual field sensitivities and no ocular diseases.
3：List of Experimental Equipment and Materials:
Octopus 900 perimeter (Haag Streit AG) was used for baseline conventional automated perimetry and subsequent tests.
4：Experimental Procedures and Operational Workflow:
Baseline fields were established using ZEST procedures. GOANNA and ZEST were then performed three times each for each scotoma type, with the order randomized to minimize fatigue and learning effects.
5：Data Analysis Methods:
Accuracy, precision, and the number of unique locations tested were measured, with results stratified by the maximum difference between a location and its neighbors (Max_d).
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