Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders.
Journal
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
ISSN: 2162-0989
Titre abrégé: Asia Pac J Ophthalmol (Phila)
Pays: China
ID NLM: 101583622
Informations de publication
Date de publication:
Historique:
entrez:
12
8
2021
pubmed:
13
8
2021
medline:
30
10
2021
Statut:
ppublish
Résumé
Deep learning (DL)-based retinal image quality assessment (RIQA) algorithms have been gaining popularity, as a solution to reduce the frequency of diagnostically unusable images. Most existing RIQA tools target retinal conditions, with a dearth of studies looking into RIQA models for optic nerve head (ONH) disorders. The recent success of DL systems in detecting ONH abnormalities on color fundus images prompts the development of tailored RIQA algorithms for these specific conditions. In this review, we discuss recent progress in DL-based RIQA models in general and the need for RIQA models tailored for ONH disorders. Finally, we propose suggestions for such models in the future.
Identifiants
pubmed: 34383719
doi: 10.1097/APO.0000000000000404
pii: 01599573-202106000-00007
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
282-288Informations de copyright
Copyright © 2021 Asia-Pacific Academy of Ophthalmology. Published by Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology.
Déclaration de conflit d'intérêts
DM is an Advisory Board member, Optomed, Finland. The remaining authors report no conflicts of interest.
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