Machine learning classifiers in glaucoma.
Machine learning is concerned with the design and development of algorithms and techniques that allow computers to "learn" patterns in data using iterative processes. Such processes can be supervised (guided by a priori group membership information) or unsupervised (guided by patterns within the data). Machine learning classifiers (MLC) are unconstrained by statistical assumptions and therefore are adaptable to complex data. Recent applications of MLC techniques to the detection and monitoring of glaucoma by analysis of visual field and optical imaging data suggest that these methods can provide improvement over currently available techniques. This article provides some background about the classification task in glaucoma and the structure and evaluation of MLCs, and it reviews MLC techniques as they have been applied to visual function and optical imaging in glaucoma research.
Bowd
Christopher
C
Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037-0946, USA. cbowd@eyecenter.ucsd.edu
Goldbaum
Michael H
MH
D001185:Artificial Intelligence; D005901:Glaucoma / Q000145:classification* / Q000503:physiopathology*; D006801:Humans; D009415:Nerve Net; D011237:Predictive Value of Tests; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014785:Vision, Ocular / Q000502:physiology