| Identifier |
20220213_nanos_posters_327 |
| Title |
A Deep Learning Model to Identify Homonymous Defects on Automated Perimetry |
| Creator |
Laura Donaldson; Aaron Tan; Luqmaan Moolla; Edward Margolin |
| Affiliation |
(LD) (AT) (LM) (EM) University of Toronto, Toronto, Canada |
| Subject |
Visual Fields |
| Description |
Homonymous visual field (VF) defects are usually an indicator of serious intracranial pathology but may often be subtle and difficult to detect. The utility of artificial intelligence (AI) applications in ophthalmology are becoming increasingly recognized. We aimed to develop an automated deep learning AI model to accurately identify homonymous VF defects from automated perimetry. |
| Date |
2022-02 |
| Language |
eng |
| Format |
application/pdf |
| Type |
Text |
| Source |
2022 North American Neuro-Ophthalmology Society Annual Meeting |
| Relation is Part of |
NANOS Annual Meeting 2022: Poster Session I: Ocular-Imaging |
| 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 2022. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright |
| ARK |
ark:/87278/s6mcgc9m |
| Setname |
ehsl_novel_nam |
| ID |
2065347 |
| Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6mcgc9m |
