Novel AI-Based Quantification of Breast Arterial Calcification to Predict Cardiovascular Risk

IMPORTANCE Women are underdiagnosed and undertreated for cardiovascular disease. Automatic quantification of breast arterial calcification on screening mammography can identify women at risk for cardiovascular disease and enable earlier treatment and management of disease. OBJECTIVE To determine whether artificial-intelligence based automatic quantification of BAC from screening mammograms predicts cardiovascular disease and mortality in a large, racially diverse, multi-institutional population, both independently and beyond traditional risk factors and ASCVD scores. DESIGN, SETTING, AND PARTICIPANTS Retrospective cohort study of 116,135 women from two healthcare systems (Emory Healthcare and Mayo Clinic Enterprise) who had screening mammograms and either experienced a major adverse cardiovascular event, death, or had at least 5 years of clinical follow-up. BAC was quantified using a novel transformer-based neural network architecture for semantic segmentation. BAC severity was categorized into four groups (no BAC, mild, moderate, and severe), with outcomes assessed using Kaplan-Meier analysis and Cox proportional-hazards models. MAIN OUTCOMES AND MEASURES Major Adverse Cardiovascular Events (MACE), including acute myocardial infarction, stroke, heart failure, and all-cause mortality, adjusted for traditional risk factors and Atherosclerotic CVD (ASCVD) risk scores. RESULTS BAC severity was independently associated with MACE after adjusting for cardiovascular risk factors, with increasing hazard ratios from mild (HR 1.18-1.22),