Mass General Brigham researchers have developed a new AI tool in collaboration with the United States Department of Veterans Affairs (VA) to probe through previously collected CT scans and identify individuals with high coronary artery calcium (CAC) levels that place them at a greater risk for cardiovascular events. Their research, published in NEJM AI, showed the tool called AI-CAC had high accuracy and predictive value for future heart attacks and 10-year mortality. Their findings suggest that implementing such a tool widely may help clinicians assess their patients’ cardiovascular risk.

Millions of chest CT scans are taken each year, often in healthy people, for example to screen for lung cancer. Our study shows that important information about cardiovascular risk is going unnoticed in these scans, said senior author Hugo Aerts, PhD, director of the Artificial Intelligence in Medicine (AIM) Program at Mass General Brigham. Our study shows that AI has the potential to change how clinicians practice medicine and enable physicians to engage with patients earlier, before their heart disease advances to a cardiac event.

Chest CT scans can detect calcium deposits in the heart and arteries that increase the risk of a heart attack. The gold standard for quantifying CAC uses “gated” CT scans, that synchronize to the heartbeat to reduce motion during the scan. But most chest CT scans obtained for routine clinical purposes are nongated. The researchers recognized that CAC could still be detected on these nongated scans, which led them to develop AI-CAC, a deep learning algorithm to probe through the nongated scans and quantify CAC to help predict the risk of cardiovascular events.

Source: https://www.massgeneralbrigham.org/en/about/newsroom/press-releases/ai-detects-hidden-heart-disease