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AI-powered Mammograms May Help Detect Heart Disease In Women
Mammograms are best known as a tool to screen for breast cancer, but a new study shows they may also reveal hidden signs of heart disease—thanks to artificial intelligence (AI).
The study, presented at the American College of Cardiology's Annual Scientific Session (ACC.25), found that AI can help analyze calcium buildup in the arteries within breast tissue, a signal of potential cardiovascular problems.
This could open the door to using mammograms not only for cancer screening but also as an early warning system for heart disease, especially in women under 60.
Mammograms are routine X-rays of the breast, recommended every one to two years for middle-aged and older women. Around 40 million are performed annually in the U.S. While breast artery calcification can sometimes be seen in these images, radiologists don't usually measure or report it, and most patients and doctors never hear about it.
But researchers from Emory University, Mayo Clinic, and other institutions believe that's a missed opportunity.
"We see a chance for women to be screened for both cancer and cardiovascular disease at the same time," said Dr. Theo Dapamede, the study's lead author. "Breast arterial calcification is a strong predictor of future heart problems, especially in women younger than 60."
Heart disease is the leading cause of death for women in the U.S., yet it often goes undiagnosed or overlooked. Adding cardiovascular screening to an already common test could help flag problems earlier—before symptoms appear.
The team used a deep-learning AI model to scan mammogram images for signs of breast arterial calcification. These appear as bright spots on the X-ray. The AI then estimated the woman's future risk of serious heart problems like heart attacks, strokes, or heart failure, based on her imaging and electronic health records.
The AI was trained on a large dataset of more than 56,000 women who had mammograms at Emory Healthcare between 2013 and 2020 and were tracked for at least five years. This gave the system enough real-world data to learn from and improve its accuracy.
The model performed well in sorting women into low, moderate, or high cardiovascular risk categories. It was especially accurate for women younger than 80, with the most value seen in women under 60—those most likely to benefit from early lifestyle changes or medical treatment.
Among women with the highest levels of calcium buildup (above 40 mm²), only 86.4% were still alive and free of heart-related problems five years later. In comparison, 95.3% of women with little or no calcium buildup (under 10 mm²) had no such issues. That's nearly a 3-times higher risk of death or a major event for those with severe calcification.
Researchers believe this AI tool could serve as a powerful early alert system—identifying women who need further evaluation by a heart doctor, even if they're visiting the clinic for a routine cancer screening.
The AI model is not yet available for public use. It must still undergo additional validation and receive approval from the U.S. Food and Drug Administration (FDA) before it can be adopted in clinics. If approved, the system could be built directly into routine mammogram screenings, helping doctors automatically assess heart risk alongside cancer detection.
Researchers are also planning to explore whether AI can use mammograms to spot early warning signs of other diseases, such as kidney disease or poor circulation in the limbs.
Review and Analysis
This research highlights a major opportunity in women's health: using existing tools in smarter ways. Since millions of mammograms are performed every year, and most women over 40 already get screened regularly, this test provides a built-in chance to check for early signs of heart disease—often before symptoms begin.
Breast arterial calcification isn't new, but until now, it hasn't been used systematically to guide care. By using AI to spot and measure it, doctors may be able to identify at-risk women earlier and potentially prevent heart attacks or strokes down the line.
What's most promising is the model's usefulness in younger women under 60, a group often overlooked when it comes to heart disease screening. These women could benefit greatly from lifestyle changes or medical treatment before more serious damage occurs.
While more research and approval are still needed, this study shows how technology—especially AI—can turn everyday medical images into powerful predictors of health beyond their original purpose. It's a promising step toward more efficient, preventative, and personalized care for women.
If you care about heart health, please read studies that apple juice could benefit your heart health, and Yogurt may help lower the death risks in heart disease.
For more information about health, please see recent studies that Vitamin D deficiency can increase heart disease risk, and results showing Zinc and vitamin B6 linked to lower death risk in heart disease.
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Reduced BMD Is Associated With Arterial Calcification
Cite this articleReduced BMD is associated with arterial calcification. Nat Rev Endocrinol 4, 121–122 (2008). Https://doi.Org/10.1038/ncpendmet0735
Share this article Get shareable linkAI-powered Mammograms: A New Window Into Heart Health
Mammograms, with the help of artificial intelligence (AI) models, may reveal much more than cancer, according to a study being presented at the American College of Cardiology's Annual Scientific Session (ACC.25). The findings highlight how these important cancer screening tools can also be used to assess the amount of calcium buildup in the arteries within breast tissue -- an indicator of cardiovascular health.
The U.S. Centers for Disease Control and Prevention recommends that middle-aged and older women get a mammogram -- an X-ray of the breast -- to screen for breast cancer every one or two years. About 40 million mammograms are performed in the United States each year. While breast artery calcifications can be seen on the resulting images, radiologists do not typically quantify or report this information to women or their clinicians. The new study, which used an AI image analysis technique not previously used on mammograms, demonstrates how AI can help fill this gap by automatically analyzing breast arterial calcification and translating the results into a cardiovascular risk score.
"We see an opportunity for women to get screened for cancer and also additionally get a cardiovascular screen from their mammograms," said Theo Dapamede, MD, PhD, a postdoctoral fellow at Emory University in Atlanta and the study's lead author. "Our study showed that breast arterial calcification is a good predictor for cardiovascular disease, especially in patients younger than age 60. If we are able to screen and identify these patients early, we can refer them to a cardiologist for further risk assessment."
Heart disease is the leading cause of death in the United States but remains underdiagnosed in women and there is also lagging awareness. Researchers said the use of AI-enabled mammogram screening tools could help identify more women with early signs of cardiovascular disease by taking better advantage of screening tests that many women routinely receive.
A buildup of calcium in blood vessels is a sign of cardiovascular damage associated with early-stage heart disease or aging. Previous studies have shown that women with calcium buildup in the arteries face a 51% higher risk of heart disease and stroke.
To develop the screening tool used for this study, researchers trained a deep-learning AI model to segment calcified vessels in mammogram images -- which appear as bright pixels on X-rays -- and calculate the future risk of cardiovascular events based on data obtained from the electronic health record data. The segmentation approach is what separates this model from previous AI models developed for analyzing breast artery calcifications. Researchers said the model is also strengthened by its use of a large dataset for training and testing, which included images and health records from over 56,000 patients who had a mammogram at Emory Healthcare between 2013 and 2020 and had at least five years of follow-up electronic health records data.
"Advances in deep learning and AI have made it much more feasible to extract and use more information from images to inform opportunistic screening," Dapamede said.
Overall findings showed the new model performed well at characterizing patients' cardiovascular risk as low, moderate or severe based on mammogram images. After calculating the risk of dying from any cause or suffering an acute heart attack, stroke or heart failure at two years and five years, the model showed that the rate of these serious cardiovascular events increased with breast arterial calcification level in two of the three age categories assessed -- women younger than age 60 and age 60-80, but not in those over age 80. This makes the tool particularly well suited for providing early warning of heart disease risk in younger women, who can benefit more from early interventions, researchers said.
The results also showed that women with the highest level of breast arterial calcification (above 40 mm2) had a significantly lower five-year rate of event-free survival than those with the lowest level (below 10 mm2). For example, 86.4% of those with the highest breast arterial calcification survived for five years compared with 95.3% of those with the lowest level of calcification. This translates to approximately 2.8 times the risk of death within five years in patients with severe breast arterial calcification compared to those with little to no breast arterial calcification.
The AI model was developed as a collaboration between Emory Healthcare and Mayo Clinic and is not currently available for use. If it passes external validation and gains approval from the U.S. Food and Drug Administration, researchers said the tool could be made commercially available for other health care systems to incorporate into routine mammogram processing and follow-up care. The researchers also plan to explore how similar AI models could be used for assessing biomarkers for other conditions, such as peripheral artery disease and kidney disease, that might be extracted from mammograms.
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