| Identifier |
20220213_nanos_posters_327 |
| Title |
A Deep Learning Model to Identify Homonymous Defects on Automated Perimetry |
| Creator |
Janet Rucker, Giulietta Riboldi, Hannah Conn, Todd Hudson, John Martone, Kelly Astudillo, John Ross Rizzo |
| 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: 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 2022. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright |
| ARK |
ark:/87278/s6mcgc9m |
| Context URL |
The NANOS Annual Meeting Neuro-Ophthalmology Collection: https://novel.utah.edu/collection/NAM/toc/ |
| Contributor Primary |
Janet Rucker |
| Contributor Secondary |
Giulietta Riboldi, Hannah Conn, Todd Hudson, John Martone, Kelly Astudillo, John Ross Rizzo |
| Setname |
ehsl_novel_nam |
| ID |
2065347 |
| Reference URL |
https://collections.lib.utah.edu/ark:/87278/s6mcgc9m |
