The researchers say the algorithm, designed specifically for female patients, could enable doctors to identify high-risk women earlier, enabling better treatment and care. Details are published today in Lancet Digital Health.
An ECG records the electrical activity of the heart and is one of the most common medical tests in the world. In their study, funded by the British Heart Foundation, the researchers used artificial intelligence to analyse over one million ECGs from 180,000 patients, of whom 98,000 were female.
In the latest study, the researchers developed a score that measures how closely an individual's ECG matches 'typical' patterns of ECGs for men and women, and which showed a range of risk for each sex. Women whose ECGs more closely matched the typical 'male' pattern - such as having an increased size of the electrical signal - tended to have larger heart chambers and more muscle mass.
Crucially, these women were also found to have a significantly higher risk of cardiovascular disease, future heart failure, and heart attacks, compared to women with ECGs more closely matching the 'typical female' ECG.
Previous evidence has shown that men tend to be at higher risk of heart disease - more accurately called cardiovascular disease - which may be due to differences in hormone profiles and lifestyle factors. Because of this, healthcare professionals and the public believe that women's risk of cardiovascular disease is low. This is even though the risk for women is also high, with women twice as likely to die of coronary heart disease, the main cause of heart attack, than from breast cancer in the UK. A recent consensus statement called cardiovascular disease the "number one killer" of women. The statement called for better diagnosis and treatment for women, as well as better female representation in clinical trials.
Dr Arunashis Sau, Academic Clinical Lecturer at Imperial College London’s National Heart and Lung Institute, and cardiology registrar at Imperial College Healthcare NHS Trust, led the research. He said:
"Our work has underlined that cardiovascular disease in females is far more complex than previously thought. In the clinic we use tests like ECGs to provide a snapshot of what's going on but as a result this may involve grouping patients by sex in a way that doesn't take into account their individual physiology. The AI enhanced ECGs give us a more nuanced understanding of female heart health - and we believe this could be used to improve outcomes for women at risk of heart disease."
Dr Fu Siong Ng, Reader in Cardiac Electrophysiology at the National Heart & Lung Institute at Imperial College London and a consultant cardiologist at Imperial College Healthcare NHS Trust and Chelsea and Westminster Hospital NHS Foundation Trust, was the senior author of the study. He said: "Many of the women identified were in fact at even higher risk than the 'average' man. If it becomes used widely, over time the AI model may reduce gender differences in cardiac care, and improve outcomes for women at risk of heart disease."
The research group recently published another paper on the related AI-ECG risk estimation model, known as AIRE, which can predict patients’ risk of developing and worsening disease from an ECG. Trials of AIRE in the NHS are already planned for late 2025. These will evaluate the benefits of implementing the model with real patients from hospitals across Imperial College Healthcare NHS Trust and Chelsea and Westminster Hospital NHS Foundation Trust. This model will be trialled in conjunction with AIRE.
Dr Sonya Babu-Narayan, Clinical Director at the British Heart Foundation, said: "Far too often, women are misdiagnosed or even dismissed by healthcare professionals, thanks to the myth that heart disease is 'only a male' issue. Even if they do receive the right diagnosis, evidence shows that women are less likely than men to receive recommended treatments."
"This study has applied powerful AI technology to ECGs, a routine, cheap and widely available heart test. Harnessing the potential of this type of research could help better identify those patients at highest risk of future heart problems and reduce the gender gap in heart care outcomes. However, one test alone will not level the playing field. Ensuring every person gets the right heart care they need when they need it will require change in every part of our healthcare system."
The research was funded by the British Heart Foundation, via a BHF Clinical Research Training Fellowship to Dr Sau, a BHF Programme Grant to Dr Fu Siong Ng, and the BHF Centre of Research Excellence at Imperial. The researchers also received support from the NIHR Imperial Biomedical Research Centre, a translational research partnership between Imperial College Healthcare NHS Trust and Imperial College London, which was awarded £95m in 2022 to continue developing new experimental treatments and diagnostics for patients.
Arunashis Sau, Ewa Sieliwonczyk, Konstantinos Patlatzoglou, Libor Pastika, Kathryn A McGurk, Antônio H Ribeiro, Antonio Luiz P Ribeiro, Jennifer E Ho, Nicholas S Peters, James S Ware, Upasana Tayal, Daniel B Kramer, Jonathan W Waks, Fu Siong Ng.
Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study.
The Lancet Digital Health, 2025. doi: 10.1016/j.landig.2024.12.003