Development and validation of a versatile foundation model for cine cardiac magnetic resonance image analysis.

Yunguan Fu^1,2, Wenjia Bai³, Weixi Yi⁴, Charlotte Manisty^5,6, Anish N Bhuva^5,6, Thomas A Treibel^5,6, James C Moon^5,6, Matthew J Clarkson⁴, Rhodri Huw Davies⁵, Yipeng Hu⁴

¹UCL Hawkes Institute, University College London, London, UK. yunguan.fu.18@ucl.ac.uk.

²InstaDeep, London, UK. yunguan.fu.18@ucl.ac.uk.

³Imperial College London, London, UK.

⁴UCL Hawkes Institute, University College London, London, UK.

⁵Institute of Cardiovascular Sciences, University College London, London, UK.

⁶Barts Heart Centre, Barts Health NHS Trust, London, UK.

PubMed DOI

Abstract

BACKGROUND: Cardiac magnetic resonance imaging is central to cardiovascular diagnosis and management, yet extracting key clinical measurements remains time-consuming, subjective, and of limited reproducibility. Current deep learning methods often require a separate model trained from scratch for each task, and generating sufficient labelled training data demands substantial clinical expertise. ME…

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