For the first time, a team of UK scientists have created nearly 4,000 anatomically accurate digital hearts to unlock fresh insights into how various factors can affect heart disease and function.

Digital twins are computer models that simulate objects or processes in the physical world. In healthcare, digital twins can predict how a patient’s disease will develop, or how patients are likely to respond to different treatments.

While digital twins can be costly and time intensive to make, recent advances in machine learning and AI have helped researchers from King’s College London, Imperial College London, and The Alan Turing Institute to create this large volume of digital twins more quickly.

The digital twin hearts were developed by using real patient data and ECG readings from both the UK Biobank, the large-scale biomedical database, as well as a cohort of patients with heart disease.

Creating cardiac digital twins at this scale has helped the researchers to discover that age and obesity cause changes in the heart’s electrical properties, which could explain why these factors are linked to a higher risk of heart disease.

They also found that differences in electrocardiogram (ECG) readings between men and women were primarily due to differences in heart size, rather than how the heart conducts electrical signals.

These insights could now help clinicians to refine treatments, such as tailoring heart devices settings or identifying new drug targets for specific groups.

“Our research shows that the potential of cardiac digital twins goes beyond diagnostics,” said Professor Steven Niederer, mission director for cardiac digital twins at The Alan Turing Institute.

“By replicating the hearts of people across the population, we have shown that digital twins can offer us deeper insights into the people at risk of heart disease. It also shows how lifestyle and gender can affect heart function.”

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Professor Pablo Lamata, report author and professor of biomedical engineering at King’s College London, also said: “These insights will help refine treatments and identify new drug targets.

“By developing this technology at scale, this research paves the way for their use in large population studies. This could lead to personalised treatments and better prevention strategies, ultimately transforming how we understand and treat heart diseases.”

The results of the research have been published in the Nature Cardiovascular Research journal.

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