Abstract

Purpose
To develop and test an artificial intelligence (AI) model to aid in differentiating pediatric pseudopapilledema from true papilledema on fundus photographs.
Design
Multicenter retrospective study.
Subjects
A total of 851 fundus photographs from 235 children (age < 18 years) with pseudopapilledema and true papilledema.
Methods
Four pediatric neuro-ophthalmologists at 4 different institutions contributed fundus photographs of children with confirmed diagnoses of papilledema or pseudopapilledema. An AI model to classify fundus photographs as papilledema or pseudopapilledema was developed using a DenseNet backbone and a tribranch convolutional neural network. We performed 10-fold cross-validation and separately analyzed an external test set. The AI model’s performance was compared with 2 masked human expert pediatric neuro-ophthalmologists, who performed the same classification task …

Date

July 1, 2024

Authors

Melinda Y Chang, Gena Heidary, Shannon Beres, Stacy L Pineles, Eric D Gaier, Ryan Gise, Mark Reid, Kleanthis Avramidis, Mohammad Rostami, Shrikanth Narayanan, Pediatric Optic Nerve Investigator Group

Journal

Ophthalmology Science

Volume

4

Issue

4

Pages

100496

Publisher

Elsevier