Cardiovascular disease research highlights AI and silent arterial damage

Research presented at the European Society of Cardiology Congress highlights new methods for detecting cardiovascular risks. A study from Tel Aviv University suggests that deep learning algorithms applied to routine mammograms could identify hypertension, stroke, and coronary artery disease in women. This approach aims to address the issue of late-stage diagnoses in female patients, as cardiovascular diseases are a leading cause of death among women but are frequently underdiagnosed.

Additionally, the international REACT study, published in The New England Journal of Medicine, found that 57.1% of asymptomatic adults in a cohort of 16,808 people from Spain and Denmark exhibited silent arterial damage (atherosclerosis). The study noted that plaque accumulation was present in approximately one in 13 adults aged 18 to 29. While prevalence was higher in men (63%) than women (51%), women showed a marked increase in risk between ages 40 and 60, coinciding with menopause. Researchers noted that traditional risk models based on age, smoking, and cholesterol often fail to identify individuals with these silent deposits.