NDLI: Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points

Content Provider	IEEE Xplore Digital Library
Author	Yousefi, S. Goldbaum, M.H. Balasubramanian, M. Tzyy-Ping Jung Weinreb, R.N. Medeiros, F.A. Zangwill, L.M. Liebmann, J.M. Girkin, C.A. Bowd, C.
Copyright Year	1964
Abstract	Machine learning classifiers were employed to detect glaucomatous progression using longitudinal series of structural data extracted from retinal nerve fiber layer thickness measurements and visual functional data recorded from standard automated perimetry tests. Using the collected data, a longitudinal feature vector was created for each patient's eye by computing the norm 1 difference vector of the data at the baseline and at each follow-up visit. The longitudinal features from each patient's eye were then fed to the machine learning classifier to classify each eye as stable or progressed over time. This study was performed using several machine learning classifiers including Bayesian, Lazy, Meta, and Tree, composing different families. Combinations of structural and functional features were selected and ranked to determine the relative effectiveness of each feature. Finally, the outcomes of the classifiers were assessed by several performance metrics and the effectiveness of structural and functional features were analyzed.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	12
File Size	1702866
Starting Page	1143
Ending Page	1154
File Format	PDF
ISSN	00189294
Volume Number	61
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Optical imaging Biomedical optical imaging Biomedical measurement Adaptive optics Optical sensors Optical fibers machine learning Biomedical engineering biomedical signal processing change detection glaucoma progression
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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