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Abstract #56482 Published in IGR 16-1
Glaucoma progression detection using structural retinal nerve fiber layer measurements and functional visual field points
Yousefi S; Goldbaum MH; Balasubramanian M; Jung TP; Weinreb RN; Medeiros FA; Zangwill LM; Liebmann JM; Girkin CA; Bowd CIEEE Transactions on Bio-Medical Engineering 2014; 61: 1143-1154
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.
Full article
Classification:
6.20 Progression (Part of: 6 Clinical examination methods)
6.6.2 Automated (Part of: 6 Clinical examination methods > 6.6 Visual field examination and other visual function tests)
6.9.2.2 Posterior (Part of: 6 Clinical examination methods > 6.9 Computerized image analysis > 6.9.2 Optical coherence tomography)
