ReviewDiagnostics (Basel, Switzerland)2022
Federated Learning in Ocular Imaging: Current Progress and Future Direction.
Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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
14 citing papers in PubMed, 42 citations in OpenAlex.
- Federated learning for privacy-preserving ophthalmic artificial intelligence: clinical applications and translational challenges.Frontiers in medicine · 2026Review
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- A Collaborative Data Sharing Platform to Accelerate Translation of Biomedical Innovations.Bioengineering (Basel, Switzerland) · 2025Article
- Distributed training of foundation models for ophthalmic diagnosis.Communications engineering · 2025Article
- Artificial intelligence for posterior capsule opacification.Frontiers in medicine · 2025Review
- New ways to use imaging data in cardiovascular research: survey of opinions on federated learning and synthetic data.European heart journal. Imaging methods and practice · 2025Article
- Foundation model-driven distributed learning for enhanced retinal age prediction.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- FedDL: personalized federated deep learning for enhanced detection and classification of diabetic retinopathy.PeerJ. Computer science · 2024Article
- Medical Imaging Applications of Federated Learning.Diagnostics (Basel, Switzerland) · 2023Review
- Retinal Scans and Data Sharing: The Privacy and Scientific Development Equilibrium.Mayo Clinic proceedings. Digital health · 2023Review
- Optical Coherence Tomography and Optical Coherence Tomography Angiography in Pediatric Retinal Diseases.Diagnostics (Basel, Switzerland) · 2023Review
- Federated learning for diagnosis of age-related macular degeneration.Frontiers in medicine · 2023Article
- Role of artificial intelligence in optical coherence tomography in myopia and pathological myopia.Taiwan journal of ophthalmologyReview
- 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
7 authors at 1 institution in 1 country.
Funding
Abstract
Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustness and generalisability of model performance, DL training strategies traditionally require extensive and diverse training datasets from various sites to be transferred and pooled into a "centralised location". However, such a data transferring process could raise practical concerns related to data security and patient privacy. Federated learning (FL) is a distributed collaborative learning paradigm which enables the coordination of multiple collaborators without the need for sharing confidential data. This distributed training approach has great potential to ensure data privacy among different institutions and reduce the potential risk of data leakage from data pooling or centralisation. This review article aims to introduce the concept of FL, provide current evidence of FL in ocular imaging, and discuss potential challenges as well as future applications.
Indexed as
Identifiers
What Socratic 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 Socratic graph.