Algorithm helps improve coronary calcium detection

Algorithm helps improve coronary calcium detection

Press releases may be edited for formatting or style | February 14, 2020 Artificial Intelligence Cardiology CT X-Ray

“This study demonstrates the growing potential of artificial intelligence-assisted technology to enhance efforts to improve the detection of heart disease, the leading cause of death in this country,” said David Goff, MD, PhD, director of the Division of Cardiovascular Sciences at NHLBI.

“It is part of an ongoing effort by researchers supported by the NHLBI to develop AI tools that can rapidly sift through vast amounts of biomedical data to identify patterns that can help detect disease and hopefully save lives.”

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“The American people and NIH have invested in these studies over decades to help us reduce the burden of heart and lung disease,” Carr added. “Thanks to carefully saving the original full fidelity images, we’re now able to use CT images and data volunteered by our participants in some cases more than a decade ago to build and train AI algorithms, techniques that did not exist when the studies began.”

Carr, who came to Vanderbilt in 2013, developed one of the first CT scanners to measure coronary calcium in 1998 while on the faculty of Wake Forest University School of Medicine in Winston-Salem, N.C.

Over the years, CT calcium “scoring” has become an important tool for understanding and determining heart disease risk.

“If you have no coronary calcium, your risk of having a heart attack in the next five years is less than 1%,” Carr said. “But if you have started to develop calcified plaque, even in your 40s and 50s, the risk can jump five- to twentyfold depending on the calcium score.

“We have not done as good a job at identifying and addressing risk factors in some populations in the United States,” he added. “Globally we need to lower barriers (to testing and treatment) to reduce the burden of heart disease worldwide.”

To broaden the application, Ivana Išgum and her colleagues, together with Carr’s input, trained an AI algorithm with CT scans with measurement of coronary calcium from the Jackson Heart Study cohort of African Americans and from the diverse participants in the NLST.

Goff noted that people should also recognize the need for other preventive efforts to fight heart disease, including physical activity, a healthy diet, regular sleep and avoidance of tobacco products.

Carr agreed. “The challenge is identifying people early in life when the prevention methods are likely to be the most effective,” he said. “By identifying individuals with relatively early coronary artery disease before they have any symptoms, we can support and encourage them to make the lifestyle changes and, if appropriate, offer them evidence-based interventions to address diabetes, elevated blood pressure, elevated cholesterol and smoking and effectively prevent or minimize the impact of heart disease.”

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