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• W4372294443 endingPage "494" @default.
• W4372294443 startingPage "485" @default.
• W4372294443 abstract "Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development.Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018.There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD." @default.
• W4372294443 created "2023-05-07" @default.
• W4372294443 creator A5044451478 @default.
• W4372294443 creator A5057264458 @default.
• W4372294443 creator A5060707909 @default.
• W4372294443 creator A5084325008 @default.
• W4372294443 date "2023-05-05" @default.
• W4372294443 modified "2023-10-14" @default.
• W4372294443 title "Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence" @default.
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• W4372294443 doi "https://doi.org/10.1080/14737159.2023.2208751" @default.
• W4372294443 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37144908" @default.
• W4372294443 hasPublicationYear "2023" @default.

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