Work

Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps

Year: 2019

Type: article

Source: Ophthalmology

DOI: 10.1016/j.ophtha.2019.09.036

Authors: Mark Christopher, Christopher Bowd, Akram Belghith, Michael H. Goldbaum, Robert N. Weinreb, Massimo A. Fazio, Christopher A. Girkin, Jeffrey M. Liebmann, Linda M. Zangwill

Institutions: University of California San Diego, University of Alabama, University of Alabama at Birmingham, Columbia University Irving Medical Center, UC San Diego Health System

Language: English

Topic: Glaucoma and retinal disorders

Subfield: Ophthalmology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: Research to Prevent Blindness, National Institutes of Health, National Eye Institute

Awards: G1610538956, G1795177522, G3335171182, Predicting and Detecting Glaucomatous Progression Using Pattern Recognition, G4092201055, Diagnosis and Monitoring of Glaucoma with Optical Coherence Tomography Angiography, African Descent and Glaucoma Evaluation (ADAGES) II: Glaucoma Progression, G5228809850, Diagnostic Innovations in Glaucoma Study (DIGS): Functional Impairment, Diagnostic Innovations in Glaucoma: Structural Assessment, G5873106719, G6337270906, G7065342431, African Descent and Glaucoma Evaluation (ADAGES) IV: Alterations of the lamina cribrosa in progression, G7804247782, Deep Learning Approaches to Detect Glaucoma and Predict Progression from Spectral Domain Optical Coherence Tomography, Diagnostic Innovations in Glaucoma Study (DIGS): Glaucoma and High Myopia, Translational Vision Research Training at UCSD, NEI Center Core Grant for Vision Research

OpenAlex ID: W2977922008

FWCI: 18.53

Cites: 48

Cited by: 179

Related to: 10