An AI Approach to Dynamic Visual Field Testing

Abstract

Visual field test results are crucial to the accuracy and efficiency of diagnosing blinding diseases such as glaucoma. Herein, a method of integrating self-organizing neural networks and empirical heuristics is used to perform visual field tests via a dynamic test strategy, which can lead to a reduction in the number of trials in a perimetric test. Experiments performed using clinical test records show that we are able to reduce by 20% to 30% the number of trials per test without much adverse effect on the accuracy of the tests.