Digital twins could transform personalised heart disease treatment

Developed by  team from King’s College London, Imperial College London and The Alan Turing Institute, the digital hearts will let researchers investigate how age, sex and lifestyle factors influence heart disease and electrical function.

Creating cardiac ‘digital twins’ at this scale has helped scientists 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. Their findings are detailed in Nature Cardiovascular Research.

The results show the opportunities that cardiac digital twins at scale offer to better understand the impact of lifestyle on the health and function of the heart across different populations.

With the help of the cardiac digital twins, they also found that differences in electrocardiogram (ECG) readings between men and women are primarily due to differences in heart size, not how the heart conducts electrical signals.

These insights could help clinicians refine treatments, such as tailoring heart device settings for men and women or identifying new drug targets for specific groups.

They hope that this deeper understanding of the heart in different groups could lead to more personalised care and treatment for those with heart conditions.

The cardiac digital twins were created using real patient’s data and ECG readings from the UK Biobank and a cohort of patients with heart disease. These then work as a digital replica of the patient’s heart which can be used to explore functions of the heart that are hard to measure directly.

Recent advances in machine learning and AI helped the researchers to create this volume of digital twins, reducing some of the manual tasks and allowing them to be built quicker.

A digital twin is a computer model that simulates an object or process in the physical world. They can be costly and time intensive to make but can offer new insights into how the physical system is or could behave.

When applied to healthcare, a digital twin could predict how a patient’s disease will develop and how patients are likely to respond to different treatments.

In a statement, Professor Pablo Lamata, report author and professor of biomedical engineering at King’s College London, 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.”