ArticleOphthalmology science
Artificial Intelligence to Differentiate Pediatric Pseudopapilledema and True Papilledema on Fundus Photographs.
Article in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-assisted diagnosis of neuro-ophthalmic disorders: a systematic review from optic neuritis to papilledema.BMC ophthalmology · 2026Pooled it
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- Artificial Intelligence in the Detection of Papilledema: A Systematic Review.Journal of clinical medicine · 2026Review
- Diagnosis of High Intracranial Pressure by Non-Optic Nerve Retinal Image Features.Translational vision science & technology · 2026Article
- Clinician-Led Code-Free Deep Learning for Detecting Papilledema and Pseudopapilledema Using Optic Disc Imaging.Translational vision science & technology · 2026Article
- Peripapillary Choroidal Vascularity Index for Differentiating Papilledema from Pseudopapilledema: A Deep Learning-Based Approach.Ophthalmology science · 2026Article
- Agreement and Accuracy of Papilledema and Pseudopapilledema Classification Among Pediatric Neuro-Ophthalmologists Using Optic Disc Photographs.Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society · 2025Observational
- Differentiation of optic disc edema and pseudopapilledema with deep learning on near-infrared reflectance images.BMC ophthalmology · 2025Article
- Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: A systematic review.PLOS digital health · 2025Article
- WHA-Net: A Low-Complexity Hybrid Model for Accurate Pseudopapilledema Classification in Fundus Images.Bioengineering (Basel, Switzerland) · 2025Article
- Distinguishing Papilledema from Pseudopapilledema in Children.Advances in ophthalmology and optometry · 2024Article
- Using multimodal imaging to improve the diagnostic accuracy and confidence in distinguishing non-arteritic anterior ischemic optic neuropathy from optic disc drusen.Frontiers in neurologyArticle
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Authors and funding
10 authors.
Funding
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. Main Outcome Measures: Accuracy, sensitivity, and specificity of the AI model compared with human experts. Results: The area under receiver operating curve of the AI model was 0.77 for the cross-validation set and 0.81 for the external test set. The accuracy of the AI model was 70.0% for the cross-validation set and 73.9% for the external test set. The sensitivity of the AI model was 73.4% for the cross-validation set and 90.4% for the external test set. The AI model's accuracy was significantly higher than human experts on the cross validation set ( Conclusions: When classifying fundus photographs of pediatric papilledema and pseudopapilledema, our AI model achieved > 90% sensitivity at detecting papilledema, superior to human experts. Due to the high sensitivity and low false negative rate, AI may be useful to triage children with suspected papilledema requiring work-up to evaluate for serious underlying neurologic conditions. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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