AI can predict heart attack or stroke in women by scanning this

By Stephen Beech

Artificial intelligence can "accurately" predict the risk of a heart attack or stroke in women by scanning mammograms, reveals new research.

A "two-for-one" screening program utilizing the state-of-the-art technology could help detect two of the leading causes of death - heart disease and breast cancer - in women worldwide, say scientists.

The study, published in the journal Heart, shows that a new machine learning model, developed by The George Institute for Global Health, can successfully predict heart disease risk in women by analyzing mammograms.

Developed in collaboration with the University of New South Wales and University of Sydney in Australia, it is the first deep learning algorithm based on only mammographic features and age to predict major cardiac events with comparable accuracy to traditional cardiovascular risk calculators.

Associate Professor Clare Arnott, of The George Institute, says that new ways to identify women at risk of cardiovascular disease (CVD) were needed, given that many women are not accessing or being offered screening in the community.

She said: “It’s a common misconception that CVD predominantly affects men, resulting in underdiagnosis and undertreatment of the condition in women.

"By integrating CV risk screening with breast screening through the use of mammograms - something many women already engage with at a stage in life when their cardiovascular risk increases - we can identify and potentially prevent two major causes of illness and death at the same time.”

The model was designed and validated using routine mammograms from more than 49,000 women in metropolitan and rural areas of Victoria, Australia, linked to individual hospital and death records.

Researchers then compared the model to traditional models based on known cardiovascular risk factors, such as blood pressure and cholesterol.

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Arnott said: "We found that our model performed just as well without the need for extensive clinical and medical data."

She says previous research has focused on certain mammographic features such as breast arterial calcification (BAC), which has been found to be associated with cardiovascular risk in some populations.

But she said relying on BAC alone has limitations. For example, BAC is less accurate at predicting CVD risk in older women.

Arnott added: “Our model is the first to use a range of features from mammographic images combined simply with age - a key advantage of this approach being that it doesn’t require additional history taking or medical record data, making it less resource intensive to implement, but still highly accurate."

Cardiovascular disease is currently the leading cause of death in women worldwide, totalling around nine million annually, or about one-in-three of all deaths in women.

Multiple studies have shown that cardiovascular disease symptoms and risk factors are "under-considered" in women, leading to fewer diagnostic tests, specialist referrals and prescriptions in women compared to men.

At the same time, mammography-based screening programs have effectively engaged women in some countries, with more than 67% of women in the UK and United States participating.

Dr. Jennifer Barraclough, of The George Institute, says that leveraging an existing risk screening process already widely utilized by women means the new model could serve as a cardiovascular risk prediction tool for women in diverse communities around the world.

Dr. Barraclough said: “We hope this technology will one day provide greater and more equitable access to screening in rural areas, as many women already benefit from mobile mammography units free of charge."

She added: “We have shown the potential of this innovative new screening tool, so we now look forward to testing the model in additional, diverse populations and understanding potential barriers to its implementation.”