ArticlePatterns (New York, N.Y.)2024
FedEYE: A scalable and flexible end-to-end federated learning platform for ophthalmology.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-enabled eye-movement and emerging multimodal frameworks for precision dyslexia screening and reading pattern analysis.Frontiers in medicine · 2026Pooled it
- Collaborative artificial intelligence for the diagnosis and management of acute ischemic stroke.Annals of medicine · 2026Review
- Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation.ACM transactions on intelligent systems and technology · 2026Article
- Federated learning for privacy-preserving ophthalmic artificial intelligence: clinical applications and translational challenges.Frontiers in medicine · 2026Review
- Predicting Intraocular Collamer Lens Vault in Myopic Patients With Shallow Anterior Chamber Using FedEYE Platform and ChatGPT.Translational vision science & technology · 2025Article
- A deep learning-driven cataract screening model derived from multicenter real-world dataset.Frontiers in medicine · 2025Article
- Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene.Nature machine intelligence · 2025Article
- Foundation model-driven distributed learning for enhanced retinal age prediction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- FedEYE: A scalable and flexible end-to-end federated learning platform for ophthalmology.Patterns (New York, N.Y.) · 2024Article
- A federal learning-driven artificial intelligence framework for fundus image myopia diagnosis.Digital healthArticle
- Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven machine learning, as a promising approach, possesses the capability to build high-quality, exact, and robust models from ophthalmic medical data. Ophthalmic medical data, however, presently exist across disparate data silos with privacy limitations, making centralized training challenging. While ophthalmologists may not specialize in machine learning and artificial intelligence (AI), considerable impediments arise in the associated realm of research. To address these issues, we design and develop FedEYE, a scalable and flexible end-to-end ophthalmic federated learning platform. During FedEYE design, we adhere to four fundamental design principles, ensuring that ophthalmologists can effortlessly create independent and federated AI research tasks. Benefiting from the design principles and architecture of FedEYE, it encloses numerous key features, including rich and customizable capabilities, separation of concerns, scalability, and flexible deployment. We also validated the applicability of FedEYE by employing several prevalent neural networks on ophthalmic disease image classification tasks.
Indexed as
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What OpenQuestion holds
Registered trials
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.