Artificial intelligence in age-related macular degeneration: state of the art and recent updates.
AMD
Age-related macular degeneration
Artificial intelligence
Deep learning
OCT
Prediction
Support vector machine
Treatment
Journal
BMC ophthalmology
ISSN: 1471-2415
Titre abrégé: BMC Ophthalmol
Pays: England
ID NLM: 100967802
Informations de publication
Date de publication:
15 Mar 2024
15 Mar 2024
Historique:
received:
05
12
2023
accepted:
06
03
2024
medline:
18
3
2024
pubmed:
16
3
2024
entrez:
16
3
2024
Statut:
epublish
Résumé
Age related macular degeneration (AMD) represents a leading cause of vision loss and it is expected to affect 288 million people by 2040. During the last decade, machine learning technologies have shown great potential to revolutionize clinical management of AMD and support research for a better understanding of the disease. The aim of this review is to provide a panoramic description of all the applications of AI to AMD management and screening that have been analyzed in recent past literature. Deep learning (DL) can be effectively used to diagnose AMD, to predict short term risk of exudation and need for injections within the next 2 years. Moreover, DL technology has the potential to customize anti-VEGF treatment choice with a higher accuracy than expert human experts. In addition, accurate prediction of VA response to treatment can be provided to the patients with the use of ML models, which could considerably increase patients' compliance to treatment in favorable cases. Lastly, AI, especially in the form of DL, can effectively predict conversion to GA in 12 months and also suggest new biomarkers of conversion with an innovative reverse engineering approach.
Identifiants
pubmed: 38491380
doi: 10.1186/s12886-024-03381-1
pii: 10.1186/s12886-024-03381-1
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
121Informations de copyright
© 2024. The Author(s).
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