Identifier |
20230314_nanos_posters_338 |
Title |
The BONSAI (Brain and Optic Nerve Study with Artificial Intelligence) Deep Learning System Can Accurately Identify Pediatric Papilledema on Standard Ocular Fundus Photographs |
Creator |
Amy (Mung Yan) Lin; Raymond Najjar; Zhiqun Tang; Daniela Cioplean; Mihaela Dragomir; Audrey Chia; Ajay Patil; Jason Peragallo; Nancy Newman; Valerie Biousse; on behalf of The BONSAI Consortium; BONSAI Study group |
Affiliation |
(AL) (JP) Emory University School of Medicine; (RN) Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore; (ZT) Visual Neuroscience Research Group, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; (MD) Ophthalmology Clinic Oftapro, Bucharest, Romania; (AC) OphthalmologClinic Oftapro, Bucharest, Romania; (AP) Paediatric and Strabismus Service, Singapore National Eye Centre, Singapore; Leeds Teaching Hospitals NHS Trust; (NN) Departments of Ophthalmology, Neurology, and Neurological Surgery, Emory University School of Medicine, Atlanta, GA; (VB) Departments of Ophthalmology and Neurology, Emory University School of Medicine, Atlanta, GA; (BC) BONSAI Consortium; (BSG) Singapore |
Subject |
Diagnostic Tests (ERG, VER, OCT, HRT, mfERG, etc); Pseudotumor Cerebri; Pediatric Neuro-ophthalmology |
Description |
The recently validated BONSAI deep learning system (DLS) was able to distinguish papilledema from normal optic discs and other optic disc abnormalities in an adult population on standard mydriatic ocular fundus photographs. Pediatric papilledema often reveals underlying severe neurologic disorders and may be difficult to diagnose, especially in young children. Ocular fundus photographs are easy to obtain even in young children and can be taken in non-ophthalmology settings. The aim of our study was to validate the BONSAI-DLS to identify papilledema and other optic disc abnormalities in children. |
Date |
2023-03-14 |
References |
1-Milea D, Najjar RP, Zhubo J, et al. Artificial intelligence to detect papilledema from ocular fundus photographs. N Engl J Med. 2020;382(18):1687-1695. 2-Toffoli D, Bruce BB, Lamirel C, et al. Feasibility and quality of nonmydriatic fundus photography in children. J AAPOS. 2011;15(6):567-72. |
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-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 2023. For further information regarding the rights to this collection, please visit: https://NOVEL.utah.edu/about/copyright |
ARK |
ark:/87278/s67y9mzr |
Setname |
ehsl_novel_nam |
ID |
2335543 |
Reference URL |
https://collections.lib.utah.edu/ark:/87278/s67y9mzr |