Currently 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 only report simplistic volumetric measures from the images to describe the heart structure and function. These measures are not able to describe the regional and subtle differences of the 3D heart shape and motion.

Therefore the Imperial team created a novel AI model called MeshHeart that builds detailed 3D models of the heart's structure and movement throughout a heartbeat. This 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 system was trained on a dataset of more than 38,000 heart scans, allowing the model to understand what a healthy heart typically looks like for different ages and sexes.

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.

Lead author Dr Mengyun Qiao 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 team now plans 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.