Can machine learning predict cardiac risk using mammography?

This editorial refers to ‘Mammography-based deep learning model for coronary artery calcification’, by S. Ahn et al., https://doi.org/10.1093/ehjci/jead307. The main purpose of breast screening programmes is to decrease mortality from breast cancer through early detection in asymptomatic women. Mammography reduces breast cancer mortality in women aged 50–74 years with smaller reductions in women aged under 50 years.1 The age and frequency of screening vary by country from annually from the age of 40–70 in the USA to every three years between the ages of 50 and 70 in the UK. Artificial intelligence algorithms have recently been developed to detect breast cancer and have been shown to be non-inferior to specialists and superior in cancer detection in combination with radiologists.2,3 The age range at which mammography is performed also coincides with the age range when statins might be prescribed for primary prevention if there are one or more cardiovascular disease (CVD) risk factors (i.e. dyslipidaemia, diabetes, hypertension, or smoking) and an estimated 10-year risk of a cardiovascular event of 10% or greater.4 Could there be a potential opportunity for opportunistic assessment of cardiovascular risk, as well as asymptomatic cancer, through novel analysis of mammographic images?