Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs

Ophthalmology

Volumn 125, Issue 8, 2018, Pages 1207-1208

  Hood, Donald C ^a   De Moraes, Carlos Gustavo ^a  

^a NONE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EDITORIAL; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; GLAUCOMATOUS OPTIC NEUROPATHY; HUMAN; LEARNING ALGORITHM; MACHINE LEARNING; MEASUREMENT ACCURACY; OPHTHALMOLOGIST; OPHTHALMOSCOPY; OPTIC DISK; OPTICAL COHERENCE TOMOGRAPHY; PREDICTIVE VALUE; PRIORITY JOURNAL; GLAUCOMA; OPTIC NERVE DISEASE; VISUAL SYSTEM EXAMINATION;

DEEP LEARNING; DIAGNOSTIC TECHNIQUES, OPHTHALMOLOGICAL; GLAUCOMA; HUMANS; OPTIC NERVE DISEASES;

EID: 85049655372     PISSN: 01616420     EISSN: 15494713     Source Type: Journal    
DOI: 10.1016/j.ophtha.2018.04.020     Document Type: Editorial

References (5)

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3. Li, Z., He, Y., Keel, S., et al. Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125 (2018), 1199–1206.
4. Hood, D.C., Raza, A.S., De Moraes, C.G., et al. Evaluation of a one-page report to aid in detecting glaucomatous damage. Transl Vis Sci Technol, 3(6), 2014, 8.
5. Muhammad, H., Fuchs, T.J., De Cuir, N., et al. Hybrid deep learning on single wide-field optical coherence tomography scans accurately classifies glaucoma suspects. J Glaucoma 26:12 (2017), 1086–1094.