New ways to use imaging data in cardiovascular research: survey of opinions on federated learning and synthetic data.

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% (n = 12) were currently using federated learning, 4% (n = 3) had previously used it, 31% (n = 21) were planning to use it, and 46% (n = 31) were neither using nor planning to use it. Highlighted benefits included data privacy and enhanced collaboration, while challenges included data heterogeneity and technical complexity. The synthetic data survey had 22 respondents: 50% (n = 11) were currently using synthetic imaging data, 36% (n = 8) expressed interest in using it, and 14% (n = 3) thought it should not be used. Amongst the respondents, 50% had created synthetic imaging data and 45% had used it in cardiovascular research. Advantages cited included privacy preservation, increased dataset size and diversity, improved data access, and reduced administrative burden. Concerns included potential biases, trust issues, privacy concerns, and the fact that the images were not real and may have limited diversity or quality.

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.