Revolutionary AI Technology Takes Mammography to New Heights in Heart Health Screening!

In an exciting development presented at the American College of Cardiology’s Annual Scientific Session (ACC.25), researchers have unveiled groundbreaking findings that illustrate how mammograms—primarily used for breast cancer detection—can also serve as powerful tools for assessing heart health. This innovative approach focuses on the evaluation of calcium buildup in artery walls within breast tissue, a significant marker of cardiovascular health.

The U.S. Centers for Disease Control and Prevention recommend that middle-aged and older women undergo mammography—an X-ray of the breast—every one or two years. Approximately 40 million mammograms are performed annually in the United States alone. Traditionally, while breast artery calcifications are visible on mammogram images, they often go unmeasured and unreported by radiologists. However, this new study introduces a cutting-edge AI image analysis technique that effectively fills the gap by quantifying breast arterial calcification and translating these findings into a cardiovascular risk score.

Dr. Theo Dapamede, a postdoctoral fellow at Emory University in Atlanta and lead author of the study, stated, “We see an opportunity for women to not only get screened for cancer but also to receive a cardiovascular assessment through their mammograms.” His research indicates that breast arterial calcification is a reliable predictor of cardiovascular disease, especially for women under 60 years of age. Early identification can lead to timely referrals to cardiologists for further evaluation, potentially saving lives.

Heart disease remains the leading cause of death among women in the United States, yet it often goes undiagnosed and lacks public awareness. By employing AI-enhanced mammographic screening, researchers believe they can spotlight early indicators of cardiovascular complications in women who are already undergoing routine screenings for breast cancer. Previous studies have established that women exhibiting calcium deposits in their arteries face a staggering 51% heightened risk of developing heart disease and stroke.

To develop this pioneering screening tool, the researchers trained a deep-learning AI model programmed to identify calcified blood vessels in mammogram images, which appear as bright pixels on the X-rays. This approach marks a significant advancement from older AI models focused on mammogram analysis. The model's accuracy benefited from a comprehensive dataset, incorporating images and health records from more than 56,000 patients who underwent mammograms at Emory Healthcare between 2013 and 2020, ensuring robust analytical results over five years of follow-up data.

The findings from the model demonstrated impressive efficacy in categorizing patients’ cardiovascular risk as low, moderate, or severe based on their mammogram results. Notably, the risk of serious cardiovascular incidents—such as heart attacks, strokes, and heart failure—escalated in tandem with levels of breast arterial calcifications in women younger than 80. This tool is particularly vital for younger women under 60, who stand to gain significantly from early interventions.

Results also highlighted a stark difference in event-free survival rates among women with differing levels of breast arterial calcification. Women displaying the highest levels of calcification (over 40 mm²) faced dramatically lower survival rates—86.4% over five years compared to 95.3% among those with minimal calcification (below 10 mm²)—indicating a 2.8 times greater risk of death within five years for those with severe calcifications.

Collaboratively developed by Emory Healthcare and Mayo Clinic, the AI model is currently awaiting external validation and Food and Drug Administration approval before it can be rolled out for commercial use. With successful validation, the researchers are optimistic that this tool could seamlessly integrate into routine mammogram processing, enhancing follow-up care. Furthermore, they are exploring the potential of similar AI models to assess biomarkers for other health conditions, such as peripheral artery and kidney disease, drawn from mammography images.

This revolutionary advancement not only underscores the importance of mammograms in cancer detection but also paves the way for a dual-purpose screening process that could change the landscape of women's health forever!