Identifier |
20230314_nanos_posters_323 |
Title |
Deep Learning Model for Static Ocular Torsion Detection Using Synthetically Generated Fundus Images |
Creator |
Chen Wang; Yunong Bai; Ashley Tsang; Yuhan Bian; Yifan Gou; Yan Lin; Matthew Zhao; Tony Wei; Jacob Desman; Casey Overby Taylor; Joseph Greenstein; Jorge Otero-Millan; Alvin Liu; Amir Kheradmand; David Zee; Kemar Green |
Affiliation |
(CW) (AT) (YB) (YG) (YL) (TW) (JD) (JG) Johns Hopkins University; (YB) Vanderbilt University; (MZ) (COT) Johns Hopkins University/Whiting School of Engineering; (JO) University of California - Berkeley; (AL) (DZ) Johns Hopkins University School of Medicine; (AK) Johns Hopins; (KG) Johns Hopkins School of Medicine |
Subject |
Adult Strabismus; Diplopia; Ocular Motility; Vestibular Disorders; Miscellaneous; Stroke |
Description |
The objective of the study is to develop deep learning models using synthetic fundus images to assess the direction (intorsion vs. extorsion) and amount (physiologic vs. pathologic) of static ocular torsion. Static ocular torsion assessment is an important clinical tool for identifying abnormalities in the vestibular-ocular-motor pathway, but current methods are time-intensive with steep learning curves for frontline providers. Advanced deep learning techniques are promising strategies to detect ocular torsion rapidly and accurately and can be applied to distinguish vestibular causes of vertical misalignment from cranial nerve palsies. |
Date |
2023-03-14 |
Language |
eng |
Format |
application/pdf |
Type |
Text |
Source |
2023 North American Neuro-Ophthalmology Society Annual Meeting |
Relation is Part of |
NANOS Annual Meeting 2023: Poster Session II: Ocular Motility Disorders and Nystagmus |
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 2023. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright |
ARK |
ark:/87278/s6d4rprk |
Setname |
ehsl_novel_nam |
ID |
2335528 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6d4rprk |