ArticleNeurology2021
Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs.
Article in Neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 64 citations in OpenAlex.
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 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
- Review
- Artificial Intelligence in Ophthalmology: Practical Applications, Subspecialty Evidence and Real-World Deployment.Cureus · 2025Review
- 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
- Visual prognosis in idiopathic intracranial hypertension: observations from a retrospective cohort in Germany.Frontiers in neurology · 2025Article
- Application of Artificial Intelligence in the Headache Field.Current pain and headache reports · 2024Review
- Diagnostic dilemma of papilledema and pseudopapilledema.International ophthalmology · 2024Review
- Deep learning-based optic disc classification is affected by optic-disc tilt.Scientific reports · 2024Article
- Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.Translational vision science & technology · 2024Article
- Classifying and quantifying changes in papilloedema using machine learning.BMJ neurology open · 2024Article
- Neurologic Dysfunction Assessment in Parkinson Disease Based on Fundus Photographs Using Deep Learning.JAMA ophthalmology · 2023Article
- Can Tele-Neuro-Ophthalmology Be Useful Beyond the Pandemic?Current neurology and neuroscience reports · 2023Review
- Deep Learning Model for Static Ocular Torsion Detection Using Synthetically Generated Fundus Images.Translational vision science & technology · 2023Article
- Deep learning system for distinguishing optic neuritis from non-arteritic anterior ischemic optic neuropathy at acute phase based on fundus photographs.Frontiers in medicine · 2023Article
- Diagnosis of Optic Disc Oedema: Fundus Features, Ocular Imaging Findings, and Artificial Intelligence.Neuro-ophthalmology (Aeolus Press) · 2023Review
Corrections and comments
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Authors and funding
15 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate the performance of a deep learning system (DLS) in classifying the severity of papilledema associated with increased intracranial pressure on standard retinal fundus photographs.
methodsA DLS was trained to automatically classify papilledema severity in 965 patients (2,103 mydriatic fundus photographs), representing a multiethnic cohort of patients with confirmed elevated intracranial pressure. Training was performed on 1,052 photographs with mild/moderate papilledema (MP) and 1,051 photographs with severe papilledema (SP) classified by a panel of experts. The performance of the DLS and that of 3 independent neuro-ophthalmologists were tested in 111 patients (214 photographs, 92 with MP and 122 with SP) by calculating the area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, and specificity. Kappa agreement scores between the DLS and each of the 3 graders and among the 3 graders were calculated.
resultsThe DLS successfully discriminated between photographs of MP and SP, with an AUC of 0.93 (95% confidence interval [CI] 0.89-0.96) and an accuracy, sensitivity, and specificity of 87.9%, 91.8%, and 86.2%, respectively. This performance was comparable with that of the 3 neuro-ophthalmologists (84.1%, 91.8%, and 73.9%,
conclusionsOur DLS accurately classified the severity of papilledema on an independent set of mydriatic fundus photographs, achieving a comparable performance with that of independent neuro-ophthalmologists. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that a DLS using mydriatic retinal fundus photographs accurately classified the severity of papilledema associated in patients with a diagnosis of increased intracranial pressure.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.