Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

Journal: American journal of ophthalmology

Published Date: May 1, 2021

Abstract

PURPOSE: To assess whether longitudinal changes in a deep learning algorithm's predictions of retinal nerve fiber layer (RNFL) thickness based on fundus photographs can predict future development of glaucomatous visual field defects.

Authors

Terry Lee

CIHR Canadian HIV Trials Network, Vancouver, British Columbia, Canada.
Alessandro A Jammal

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

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

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

Keywords

Adult Aged Deep Learning Female Follow-Up Studies Fundus Oculi Glaucoma Humans Male Middle Aged Nerve Fibers Photography Retinal Ganglion Cells Retrospective Studies Tomography, Optical Coherence Visual Field Tests Visual Fields

External Resources

View on PubMed Access via DOI PubMed (33422463)

Predicting Glaucoma Development With Longitudinal Deep Learning Predictions From Fundus Photographs.

Abstract

Authors

Keywords

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