Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.

Journal: American journal of ophthalmology

PMID: 38703802

Abstract

PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.

Authors

Sayan Mandal

Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA.
Alessandro A Jammal

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

Bascom Palmer Eye Institute (A.A.J., D.M., F.A.M.), University of Miami, Miami, Florida, USA.
Felipe A Medeiros

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

Keywords

Aged Aging Algorithms Deep Learning Disease Progression Female Follow-Up Studies Glaucoma Glaucoma, Open-Angle Humans Intraocular Pressure Male Middle Aged Nerve Fibers Neural Networks, Computer Optic Disk Optic Nerve Diseases Retinal Ganglion Cells Retrospective Studies Tomography, Optical Coherence Visual Fields

External Resources

View on PubMed Access via DOI PubMed (38703802)

Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals