| 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 |
| Affiliation |
(CV) (RN) (ZT) (DM) Singapore Eye Research Institute, Singapore, Singapore; (XX) (LY) Institute of High Performance Computing (A*STAR), Singapore, Singapore; (JLL) (SS) (ST) (DT) (TYW) Singapore National Eye Center, Singapore, Singapore; (LM) UC Berkeley, Berkeley, California; (NN) (VB) Emory School of Medicine, Atlanta, Georgia |
| 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: NANOS Annual Meeting Collection: https://novel.utah.edu/collection/nanos-annual-meeting-collection/ |
| Publisher |
North American Neuro-Ophthalmology Society |
| Holding Institution |
Spencer S. Eccles Health Sciences Library, University of Utah |
| 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 |
| Setname |
ehsl_novel_nam |
| ID |
1675897 |
| Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6k1310p |
