Imperial researchers create AI model to generate heart animations

Researchers at Imperial College London have developed a generative AI model that creates realistic and personalised animations of human hearts to help identify abnormalities.

The Imperial team used images of real human hearts from more than 38,000 UK Biobank participants to develop MeshHeart, which builds detailed 3D models of the heart’s structure and movement throughout a heartbeat.

Their research was published in Nature Machine Intelligence on 19 May 2025.

Dr Mengyun Qiao, lead author, said: “As we move towards more personalised healthcare, MeshHeart offers a new way to understand how each individual’s heart moves and functions.

“By comparing a person’s heart to a personalised ‘healthy’ version, we hope to catch early and subtle signs of disease that might be missed.

“It’s about bringing precision and detail to cardiovascular care.”

The work is aimed at helping to tackle cardiovascular disease, which is estimated to cause a quarter of all deaths in the UK.

Cardiac magnetic resonance (CMR) is the best way to look at the heart in detail for diagnosing cardiovascular disease.

However, existing analysis techniques for CMR images are not able to describe the regional and subtle differences of the 3D heart shape and motion.

MeshHeart uses a type of deep learning called ‘graph convolutional networks’ to understand the shape of the heart and a transformer model to capture how it changes over time.

The model can generate a personalised normal heart model based on a particular individual’s clinical information.

By comparing an individual’s actual heart model to their personalised healthy reference, the system can therefore detect differences that may indicate underlying heart conditions or potential health risks.

Researchers at Imperial are curating more CMR datasets containing different disease types and acquired from different hospital sites, to further evaluate the shape modelling performance of the developed model.

They plan to link MeshHeart with hospital records to create even more accurate, personalised heart models.

They also aim to test how the heart might respond to treatments or medication by simulating future changes, helping doctors make better-informed decisions.

In May 2024, Digital Health News reported that researchers are building digital twin heart models for a group of NHS patients with pulmonary arterial hypertension, a life-threatening cardiovascular disease which causes severe breathlessness, heart failure and recurrent hospitalisation.

Creating the digital twin involves designing and building a real-time virtual counterpart of a patient’s heart using health data, including medical records, hospital scans and information collected from wearable and implanted monitors.