Medical researchers have identified a potential method to detect cardiovascular disease in women through analysis of mammograms already performed for breast cancer screening. The approach uses artificial intelligence to identify indicators of heart conditions in imaging that women routinely undergo, potentially addressing significant gaps in cardiovascular disease diagnosis among female patients.
Israeli doctors examined approximately 97,364 scans from 29,921 women with an average age of 54 to develop and test the methodology. Among this population, 16% had high blood pressure, 2.5% had coronary heart disease, and 2.5% had experienced a stroke. The machine-learning model demonstrated varying reliability rates when identifying these conditions: 86% accuracy for stroke detection, 79% for high blood pressure, and 78% for coronary heart disease. The results remained consistent regardless of patient age or concurrent cancer diagnoses.
Researchers presented their findings at the European Society of Cardiology’s annual congress. Experts emphasized that cardiovascular disease remains the leading cause of death in women worldwide, yet frequently goes undiagnosed or is detected only at advanced stages. The dual-purpose screening approach could leverage existing mammography infrastructure without requiring additional imaging procedures, potentially reaching millions of women annually for early cardiovascular risk assessment.
Medical professionals responding to the research described it as compelling but noted that establishing accuracy and clinical reliability remains necessary before widespread implementation. Current efforts focus on improving the artificial intelligence model’s precision, reducing false results, and expanding detection capabilities to include additional cardiovascular conditions. Experts highlighted that this approach could address persistent underdiagnosis and undertreatment of heart disease in women while capitalizing on existing screening programs.
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