2014
DOI: 10.1109/tbme.2013.2295605
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Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points

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 f… Show more

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Cited by 97 publications
(61 citation statements)
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References 40 publications
(37 reference statements)
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“…Recent advances in machine learning techniques provide new approaches for glaucoma-related diagnosis and progression detection based on learning from a pool of data [1115]. 6) Variational Bayesian independent component analysis mixture model (VIM) for glaucoma defect pattern identification and progression detection is a machine learning classification-based approach developed by our group [1618].…”
Section: Introductionmentioning
confidence: 99%
“…Recent advances in machine learning techniques provide new approaches for glaucoma-related diagnosis and progression detection based on learning from a pool of data [1115]. 6) Variational Bayesian independent component analysis mixture model (VIM) for glaucoma defect pattern identification and progression detection is a machine learning classification-based approach developed by our group [1618].…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning techniques have been widely used in biomedical applications [1]–[14]. Recent advances in data analysis and a significant growth in available database size have promoted classification methods that are capable of identifying previously hidden clusters and patterns in available datasets.…”
Section: Introductionmentioning
confidence: 99%
“…Pattern classification is a broad subject with applications in many different fields including biomedicine [19]. Recognizing patterns hidden in data sets can result in extracting valuable knowledge about the data.…”
Section: Introductionmentioning
confidence: 99%