A Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs

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Identifier 20210221_nanos_posters_106
Title A Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs
Creator Caroline Vasseneix, Raymond Najjar, Xinxing Xu, Zhiqun Tan, Jing Liang Loo, Shweta Singhal, Sharon Tow, Leonard Milea, Daniel Ting, Liu Yong, Tien Yin Wong, Nancy Newman, Valerie Biousse, Dan Milea
Subject High Intracranial Pressure/Headache; Pseudotumor Cerebri
Description Papilledema severity is an important predictive factor for visual outcomes of patients with intracranial hypertension. The aim of our study was to evaluate the performance of an artificial intelligence deep learning system (DLS) to classify the severity of papilledema on standard mydriatic retinal fundus photographs.
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 II: Idiopathic Intracranial Hypertension (IIH)
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/s6k1310p
Context URL The NANOS Annual Meeting Neuro-Ophthalmology Collection: https://novel.utah.edu/collection/NAM/toc/
Contributor Primary Caroline Vasseneix
Contributor Secondary Raymond Najjar, Xinxing Xu, Zhiqun Tan, Jing Liang Loo, Shweta Singhal, Sharon Tow, Leonard Milea, Daniel Ting, Liu Yong, Tien Yin Wong, Nancy Newman, Valerie Biousse, Dan Milea
Setname ehsl_novel_nam
ID 1675897
Reference URL https://collections.lib.utah.edu/ark:/87278/s6k1310p
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