Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier

Ophthalmology

Volumn 123, Issue 9, 2016, Pages 1974-1980

  Asaoka, Ryo ^a   Murata, Hiroshi ^a   Iwase, Aiko ^b   Araie, Makoto ^a,c  

^a UNIVERSITY OF TOKYO   (Japan)

^b Tajimi Iwase Eye Clinic   (Japan)

^c Mutual Aid Association of Public School Teachers   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ADULT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFIER; COHORT ANALYSIS; CONTROLLED STUDY; DEEP LEARNING CLASSIFIER; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; DIAGNOSTIC VALUE; FEMALE; GRADIENT BOOSTING; HUMAN; HUMPHREY PERIMETER; INTERMETHOD COMPARISON; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; MIDDLE AGED; OPEN ANGLE GLAUCOMA; PERIMETRY; PRIORITY JOURNAL; RANDOM FOREST; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VISUAL FIELD; AGED; AREA UNDER THE CURVE; CASE CONTROL STUDY; GLAUCOMA, OPEN-ANGLE; OPTIC NERVE DISEASES; OPTICAL COHERENCE TOMOGRAPHY; PATHOPHYSIOLOGY; PHYSIOLOGY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; VISION DISORDERS;

ADULT; AGED; AREA UNDER CURVE; CASE-CONTROL STUDIES; FEMALE; GLAUCOMA, OPEN-ANGLE; HUMANS; MALE; MIDDLE AGED; OPTIC NERVE DISEASES; ROC CURVE; SENSITIVITY AND SPECIFICITY; TOMOGRAPHY, OPTICAL COHERENCE; VISION DISORDERS; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 84982243237     PISSN: 01616420     EISSN: 15494713     Source Type: Journal    
DOI: 10.1016/j.ophtha.2016.05.029     Document Type: Article

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