ArticleEuropean heart journal. Imaging methods and practice2025
New ways to use imaging data in cardiovascular research: survey of opinions on federated learning and synthetic data.
Article in European heart journal. Imaging methods and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Addressing the Challenges in Using Synthetic Data for Health Research: Application to Cardiology.JMIR cardio · 2026Article
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Federated learning and the creation of synthetic data are emerging tools, which may enhance the use of imaging data in cardiovascular research. This study sought to understand the perspectives of cardiovascular imaging researchers on the potential benefits and challenges associated with these technologies. Methods and results: The British Heart Foundation Data Science Centre conducted a series of online surveys and a virtual workshop to gather insights from stakeholders involved in cardiovascular imaging research about federated learning and synthetic data generation. The federated learning survey included 67 respondents: 18% ( Conclusion: Federated learning and synthetic data offer opportunities for advancing cardiovascular imaging research by addressing data privacy concerns and expanding data availability. However, challenges must be addressed to realize their full potential.
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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.