title: 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.
authors.affiliation: Birkbeck College, University of London, Malet Street, London, WC1E 7HX, United Kingdom.
authors.forename: K W
authors.initials: KW
authors.lastname: Cho
authors.forename: X
authors.initials: X
authors.lastname: Liu
authors.forename: G
authors.initials: G
authors.lastname: Loizou
authors.forename: J X
authors.initials: JX
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003951:Diagnostic Errors; D005901:Glaucoma / Q000175:diagnosis; D006801:Humans; D008403:Mass Screening; D016571:Neural Networks, Computer; D012985:Software Design; D058609:Visual Field Tests / Q000379:methods* / Q000706:statistics & numerical data; D014794:Visual Fields