Applications of deep learning in detection of glaucoma: A systematic review.

Journal Article (Journal Article;Systematic Review)

Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical practice. Artificial intelligence (AI) is an expanding field that offers the potential to improve diagnosis and screening for glaucoma with minimal reliance on human input. Deep learning (DL) algorithms have risen to the forefront of AI by providing nearly human-level performance, at times exceeding the performance of humans for detection of glaucoma on structural and functional tests. A succinct summary of present studies and challenges to be addressed in this field is needed. Following PRISMA guidelines, we conducted a systematic review of studies that applied DL methods for detection of glaucoma using color fundus photographs, optical coherence tomography (OCT), or standard automated perimetry (SAP). In this review article we describe recent advances in DL as applied to the diagnosis of glaucoma and glaucoma progression for application in screening and clinical settings, as well as the challenges that remain when applying this novel technique in glaucoma.

Full Text

Duke Authors

Cited Authors

  • Mirzania, D; Thompson, AC; Muir, KW

Published Date

  • July 2021

Published In

Volume / Issue

  • 31 / 4

Start / End Page

  • 1618 - 1642

PubMed ID

  • 33274641

Electronic International Standard Serial Number (EISSN)

  • 1724-6016

Digital Object Identifier (DOI)

  • 10.1177/1120672120977346


  • eng

Conference Location

  • United States