Predicting macular edema recurrence from spatio-temporal signatures in optical coherence tomography images

IEEE Transactions on Medical Imaging

Volumn 36, Issue 9, 2017, Pages 1773-1783

  Vogl, Wolf Dieter ^a   Waldstein, Sebastian M ^a   Gerendas, Bianca S ^a   Schmidt Erfurth, Ursula ^a   Langs, Georg ^a  

^a MEDICAL UNIVERSITY OF VIENNA   (Austria)

Author keywords

biomarkers; optical coherence tomography; Predictive models

Indexed keywords

BIOMARKERS; COHERENT LIGHT; DECISION TREES; FORECASTING; LEARNING ALGORITHMS; LEARNING SYSTEMS; OPHTHALMOLOGY; PATIENT TREATMENT; REGRESSION ANALYSIS; TOMOGRAPHY;

HIGH-DIMENSIONAL FEATURE SPACE; MACHINE LEARNING APPROACHES; OBSERVABLE VARIABLES; PREDICTIVE MODELS; QUANTITATIVE CHARACTERISTICS; RECEIVER OPERATING CHARACTERISTIC CURVES; SPATIO TEMPORAL FEATURES; SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHIES;

OPTICAL TOMOGRAPHY;

ACCURACY; ARTICLE; BRANCH RETINAL VEIN OCCLUSION; CENTRAL RETINA VEIN OCCLUSION; HUMAN; MACHINE LEARNING; MACULAR EDEMA; MAJOR CLINICAL STUDY; PREDICTION; QUANTITATIVE ANALYSIS; RANDOM FOREST; RECURRENT DISEASE; RETINAL THICKNESS; SENSITIVITY AND SPECIFICITY; SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY; TREATMENT OUTCOME; OPTICAL COHERENCE TOMOGRAPHY; RETINA; RETINA VEIN OCCLUSION; VISUAL ACUITY;

HUMANS; MACULAR EDEMA; RETINA; RETINAL VEIN OCCLUSION; TOMOGRAPHY, OPTICAL COHERENCE; VISUAL ACUITY;

EID: 85029699583     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2017.2700213     Document Type: Article

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