AI-Powered ECGs: Identifying Heart Disease Risk in Women, Study Reveals - Video

A new AI model can flag female patients who are at higher risk of heart disease based on an electrocardiogram (ECG). The researchers say the algorithm, designed specifically for female patients, could enable doctors to identify high-risk women earlier, enabling better treatment and care. The research has been published 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. 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.

Ref: Artificial intelligence-enhanced electrocardiography for the identification of a sex-related cardiovascular risk continuum: a retrospective cohort study . Sau, Arunashis et al. The Lancet Digital Health, Volume 7, Issue 3, e184 - e194. 10.1016/j.landig.2024.12.003