Artificial Intelligence to Classify Fundus Photographs of Pediatric Pseudopapilledema and True Papilledema
Creator
Melinda Chang; Gena Heidary; Shannon Beres; Stacy Pineles; Eric Gaier; Ryan Gise; Kleanthis Avramidis; Mohammad Rostami; Shrikanth Narayanan
Affiliation
(MC) Children's Hospital of Los Angeles, Keck SOM of USC; (GH) (EG) Boston Children's Hospital, Harvard Medical School; (SB) Stanford University; (SP) UCLA; (RG) Boston Children's Hospital; (KA) (MR) (SN) University of Southern California
Subject
Pediatric Neuro-ophthalmology
Description
Differentiating pseudopapilledema and papilledema in children represents a significant diagnostic dilemma. Although various ophthalmic imaging modalities have been studied, there is no single technique, when interpreted by human observers, that may be relied upon for accurate diagnosis. The purpose of this study was to use artificial intelligence to develop a deep learning model to differentiate pediatric pseudopapilledema and papilledema using fundus photographs.
Date
2023-03
References
Chang MY, Binenbaum G, Heidary G, Morrison DG, Galvin JA, et al. Imaging Methods for Differentiating Pediatric Papilledema from Pseudopapilledema: A Report by the American Academy of Ophthalmology. Ophthalmology. 2020 Oct;127(10):1416-1423.
Language
eng
Format
video/mp4
Type
Image/MovingImage
Source
49th Annual North American Neuro-Ophthalmology Society Annual Meeting
