AI-Powered Mammograms: A Dual Screening Tool for Breast Cancer and Heart Health

AI-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 mm) had a significantly lower five-year rate of event-free survival than those with the lowest level (below 10 mm). 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.

AI technology in mammograms could predict heart disease in women

American College of CardiologyMar 20 2025 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. 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." Theo Dapamede, MD, PhD, postdoctoral fellow at Emory University in Atlanta and study's lead author 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. American College of Cardiology

AI-powered mammograms provide 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, Ph.D., 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 of 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 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 mm) had a significantly lower five-year rate of event-free survival than those with the lowest level (below 10 mm). 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.

Mammograms Can Help Assess Women's Heart Health As Well

TUESDAY, March 25, 2025 (HealthDay News) -- Mammograms can be used to screen for more than just breast cancer, researchers say. The X-ray breast scans also can be used to assess calcium deposits in arteries, which is an indicator of heart health, researchers are scheduled to report Monday at a meeting of the American College of Cardiology in Chicago. Using artificial intelligence (AI), researchers were able to analyze calcium buildup in the arteries within breast tissue, and link those findings to women's five-year risk of death, results show. Women with severe levels of breast arterial calcification had nearly three times the risk of death within five years as those with little to no calcium buildup, researchers report. "We see an opportunity for women to get screened for cancer and also additionally get a cardiovascular screen from their mammograms," lead researcher Dr. Theo Dapamede, a postdoctoral fellow at Emory University in Atlanta, said in a news release. "Our study showed that breast arterial calcification is a good predictor for cardiovascular disease, especially in patients younger than age 60," Dapamede said. "If we are able to screen and identify these patients early, we can refer them to a cardiologist for further risk assessment." Previous studies have shown that women with calcium buildup in their arteries have a 51% higher risk of heart disease and stroke, researchers said in background notes. Around 40 million mammograms are performed in the U.S. each year, and breast artery calcifications can be seen on the resulting images, showing up as bright pixels on the X-rays, researchers noted. However, radiologists do not usually analyze or report these calcifications, instead focusing solely on evidence of breast cancer, researchers said. For this study, researchers trained AI to look for calcified arteries in mammogram images. The AI scanned images and health records for more than 56,000 patients who had a mammogram at Emory between 2013 and 2020. The AI found that the risk of heart attack, stroke or heart failure increased with breast calcification levels in women younger than 60 and women 60 to 80, but not in women 80 or older. Results also showed that women with the highest levels of breast arterial calcification had a significantly lower five-year rate of survival without a heart attack, stroke or heart failure. For example, about 86% of those with the highest levels survived for five years compared with 95% of the lowest levels, researchers said. That translates to about 2.8 times the risk of death within five years for patients with severe breast arterial calcification, results show. If the AI receives approval from the U.S. Food and Drug Administration (FDA), it could be made available to other health care systems to incorporate into their routine mammography programs, researchers said. They also plan to explore whether AI also could use mammogram imaging to check for other conditions like peripheral artery disease and kidney disease. Findings presented at medical meetings should be considered preliminary until published in a peer-reviewed journal. SOURCE: American College of Cardiology, news release, March 20, 2025