From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

Journal: Ophthalmology

PMID: 30578810

Abstract

PURPOSE: Previous approaches using deep learning (DL) algorithms to classify glaucomatous damage on fundus photographs have been limited by the requirement for human labeling of a reference training set. We propose a new approach using quantitative spectral-domain (SD) OCT data to train a DL algorithm to quantify glaucomatous structural damage on optic disc photographs.

Authors

Felipe A Medeiros

Duke Eye Center, Department of Ophthalmology, Duke University, Durham, North Carolina, United States.
Alessandro A Jammal

Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina.
Atalie C Thompson

Vision, Imaging and Performance (VIP) Laboratory, Duke Eye Center and Department of Ophthalmology, Duke University, Durham, North Carolina.

Keywords

Aged Aged, 80 and over Algorithms Cross-Sectional Studies Deep Learning Female Fundus Oculi Glaucoma, Open-Angle Gonioscopy Humans Intraocular Pressure Male Middle Aged Nerve Fibers Optic Disk Optic Nerve Diseases Photography Retinal Ganglion Cells ROC Curve Tomography, Optical Coherence Tonometry, Ocular Visual Acuity Visual Field Tests Visual Fields

External Resources

View on PubMed Access via DOI PubMed (30578810)

From Machine to Machine: An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs.

Abstract

Authors

Keywords

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