Learning from data: Recognizing glaucomatous defect patterns and detecting progression from visual field measurements

IEEE Transactions on Biomedical Engineering

Volumn 61, Issue 7, 2014, Pages 2112-2124

  Yousefi, Siamak ^a   Goldbaum, Michael H ^a   Balasubramanian, Madhusudhanan ^a,b   Medeiros, Felipe A ^a   Zangwill, Linda M ^a   Liebmann, Jeffrey M ^c   Girkin, Christopher A ^d   Weinreb, Robert N ^a   Bowd, Christopher ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b University of Memphis   (United States)

^c NEW YORK UNIVERSITY   (United States)

^d UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

Author keywords

Data analysis; Glaucoma; Machine learning; Progression detection; Visual field

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA REDUCTION; DEFECTS; IMAGE SEGMENTATION; LEARNING SYSTEMS; OPHTHALMOLOGY; PIPELINES;

DETECTION SOFTWARE; EXPECTATION - MAXIMIZATIONS; GLAUCOMA; HIERARCHICAL APPROACH; MIXTURE OF GAUSSIANS; VISUAL FIELD DEFECTS; VISUAL FIELD TESTS; VISUAL FIELDS;

VISION;

ADULT; ARTICLE; BIOMICROSCOPY; CAMERA; CLINICAL ASSESSMENT; CONTROLLED STUDY; DATA ANALYSIS; DATA ANALYSIS SOFTWARE; DIAGNOSTIC TEST ACCURACY STUDY; DISEASE COURSE; DISEASE SEVERITY; FEMALE; GLAUCOMA; GOLDMANN APPLANATION TONOMETER; GONIOSCOPY; HUMAN; INTRAOCULAR PRESSURE; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MEASUREMENT ACCURACY; MEASUREMENT ERROR; OPTIC DISK; PERIMETRY; SLIT LAMP; VISUAL ACUITY; VISUAL FIELD; VISUAL FIELD DEFECT; AGED; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; MIDDLE AGED; PATHOPHYSIOLOGY; PHYSIOLOGY; PROCEDURES; STATISTICAL MODEL; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; ARTIFICIAL INTELLIGENCE; DISEASE PROGRESSION; FEMALE; GLAUCOMA; HUMANS; LINEAR MODELS; MALE; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 84903157123     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2014.2314714     Document Type: Article

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