Cardiac Digital Twins: AI-Powered Insights Revolutionize Heart Disease Research

Thousands of cardiac 'digital twins' offer new insights into the heart

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. The results, published today in Nature Cardiovascular Research, 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. More broadly, 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. Professor Steven Niederer, senior author and Chair in Biomedical Engineering at Imperial College London who undertook the research whilst at King's College London, said: "Our research shows that the potential of cardiac digital twins goes beyond diagnostics. "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." 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." Dr Shuang Qian, lead author and visiting research associate at The Centre for Medical Engineering, King's College London, said: "The digital heart models we've built lay the foundation for the next step in our research -- linking heart function to our genes. This could help us understand how genetic variations influence heart function in a way that's never been done before. This could lead to more precise and personalised care for patients in the future."

Cardiac 'digital twins' provide clues to more personalized heart treatments

King's College LondonMay 16 2025 For the first time, researchers from King's College London, Imperial College London and The Alan Turing Institute, have created over 3,800 anatomically accurate digital hearts to 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. The results, published today in Nature Cardiovascular Research, 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. More broadly, 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. Professor Steven Niederer, senior author and Chair in Biomedical Engineering at Imperial College London who undertook the research whilst at King's College London, said: "Our research shows that the potential of cardiac digital twins goes beyond diagnostics. "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." 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 personalized treatments and better prevention strategies, ultimately transforming how we understand and treat heart diseases." Professor Pablo Lamata, report author and professor of biomedical engineering at King's College London Dr. Shuang Qian, lead author and visiting research associate at The Centre for Medical Engineering, King's College London, said: "The digital heart models we've built lay the foundation for the next step in our research - linking heart function to our genes. This could help us understand how genetic variations influence heart function in a way that's never been done before. This could lead to more precise and personalised care for patients in the future." King's College London Journal reference: Qian, S., et al. (2025). Developing cardiac digital twin populations powered by machine learning provides electrophysiological insights in conduction and repolarization. Nature Cardiovascular Research. doi.org/10.1038/s44161-025-00650-0.

Thousands of cardiac 'digital twins' offer new insights into the heart

For the first time, researchers from King's College London, Imperial College London and The Alan Turing Institute, have created over 3,800 anatomically accurate digital hearts to 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. The results, published in Nature Cardiovascular Research, 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 personalized care and treatment for those with heart conditions. The cardiac digital twins were created using real patient 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. More broadly, 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 health care, a digital twin could predict how a patient's disease will develop and how patients are likely to respond to different treatments. Professor Steven Niederer, senior author and Chair in Biomedical Engineering at Imperial College London, who undertook the research while at King's College London, said, "Our research shows that the potential of cardiac digital twins goes beyond diagnostics. "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." 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 personalized treatments and better prevention strategies, ultimately transforming how we understand and treat heart diseases." Dr. Shuang Qian, lead author and visiting research associate at the Center for Medical Engineering, King's College London, said, "The digital heart models we've built lay the foundation for the next step in our research -- linking heart function to our genes. This could help us understand how genetic variations influence heart function in a way that's never been done before. "This could lead to more precise and personalized care for patients in the future."