Applying Neural Networks to Macular OCTA to Differentiate LHON Vasculature from Normal Vasculature

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Identifier 20210222_nanos_posters_143
Title Applying Neural Networks to Macular OCTA to Differentiate LHON Vasculature from Normal Vasculature
Creator William Sultan; Giulia Amore; Kashif Iqbal; Samuel Asanad; Rustum Karanji; Alfredo Sadun
Affiliation (WS) (AS) Doheny Eye Institute, Pasadena, California; (GA) IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy; (KI) University of California, Riverside, California; (SA) University of Maryland, Baltimore, Maryland; (RK) University of Ottawa Eye Institute, Ottawa, Canada
Subject Diagnostic Tests (ERG, VER, OCT, HRT, mfERG, etc); Retina; Orbit/Ocular Pathology
Description Neural networks have the capacity to recognize patterns in data that are typically too complex for a human to make a determination. Using artificial intelligence, we are gaining the ability to discern the presence of optic neuropathy related mitochondrial disease using data based on the vasculature architecture in the retina.
Date 2021-02
Language eng
Format application/pdf
Type Text
Source 2021 North American Neuro-Ophthalmology Society Annual Meeting
Relation is Part of NANOS Annual Meeting 2021: Poster Session III: New Diagnostic Measurement Techniques
Collection Neuro-ophthalmology Virtual Education Library: NOVEL http://NOVEL.utah.edu
Publisher Spencer S. Eccles Health Sciences Library, University of Utah
Holding Institution North American Neuro-Ophthalmology Association. NANOS Executive Office 5841 Cedar Lake Road, Suite 204, Minneapolis, MN 55416
Rights Management Copyright 2021. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright
ARK ark:/87278/s6x40t6w
Context URL The NANOS Annual Meeting Neuro-Ophthalmology Collection: https://novel.utah.edu/collection/NAM/toc/
Setname ehsl_novel_nam
ID 1675933
Reference URL https://collections.lib.utah.edu/ark:/87278/s6x40t6w
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