Glaucoma management in the era of artificial intelligence.
Glaucoma
Imaging
Optic Nerve
Journal
The British journal of ophthalmology
ISSN: 1468-2079
Titre abrégé: Br J Ophthalmol
Pays: England
ID NLM: 0421041
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
received:
30
07
2019
revised:
07
09
2019
accepted:
05
10
2019
pubmed:
24
10
2019
medline:
27
10
2020
entrez:
24
10
2019
Statut:
ppublish
Résumé
Glaucoma is a result of irreversible damage to the retinal ganglion cells. While an early intervention could minimise the risk of vision loss in glaucoma, its asymptomatic nature makes it difficult to diagnose until a late stage. The diagnosis of glaucoma is a complicated and expensive effort that is heavily dependent on the experience and expertise of a clinician. The application of artificial intelligence (AI) algorithms in ophthalmology has improved our understanding of many retinal, macular, choroidal and corneal pathologies. With the advent of deep learning, a number of tools for the classification, segmentation and enhancement of ocular images have been developed. Over the years, several AI techniques have been proposed to help detect glaucoma by analysis of functional and/or structural evaluations of the eye. Moreover, the use of AI has also been explored to improve the reliability of ascribing disease prognosis. This review summarises the role of AI in the diagnosis and prognosis of glaucoma, discusses the advantages and challenges of using AI systems in clinics and predicts likely areas of future progress.
Identifiants
pubmed: 31640973
pii: bjophthalmol-2019-315016
doi: 10.1136/bjophthalmol-2019-315016
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
301-311Informations de copyright
© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.
Déclaration de conflit d'intérêts
Competing interests: MJAG and AHT are co-founders of Abyss Processing.