A tool that leverages artificial intelligence (AI) to comb through previously collected computed tomography (CT) scans and identify individuals with high coronary artery calcium (CAC) levels holds promise for treating veterans who are at higher risk than other populations of cardiovascular problems, according to recently published research.
The tool, dubbed “AI-CAC,” had high accuracy and predictive value for future heart attacks and 10-year mortality in an exclusively veteran population.1 Their findings suggest that implementing such a tool widely may help clinicians assess their patients’ cardiovascular risk.
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 from the Division of Cardiology, Veterans Affairs (VA) Long Beach Healthcare System, Long Beach, CA, and other institutions recognized that CAC could still be detected on these nongated scans, which prompted the development of AI-CAC, a deep learning algorithm to probe through the nongated scans and quantify CAC to help predict the risk of cardiovascular events. The researchers trained the AI on chest CT scans collected as part of the usual care of veterans from 98 medical centers across the VA national healthcare system, and then tested AI-CAC’s performance on 8052 CT scans to simulate CAC screening in routine imaging tests.
The AI-CAC model was 89.4% accurate at determining whether a scan contained CAC. For those with CAC present, the model was 87.3% accurate at determining whether the score was higher or lower than 100, indicating a moderate cardiovascular risk. AI-CAC was also predictive of 10-year all-cause mortality—those with a CAC score >400 had a 3.49 times higher risk of death over a 10-year period than patients with a score of zero. Of the patients the model identified as having very high CAC scores, 4 cardiologists verified that 99.2% would benefit from lipid-lowering therapy.
“At present, VA imaging systems contain millions of nongated chest CT scans that may have been taken for another purpose, around 50,000 gated studies. This presents an opportunity for AI-CAC to leverage routinely collected nongated scans for purposes of cardiovascular risk evaluation and to enhance care,” first author Raffi Hagopian, MD, a cardiologist and researcher in the Applied Innovations and Medical Informatics group at the VA Long Beach Healthcare System, said in a prepared statement about the findings.2 “Using AI for tasks like CAC detection can help shift medicine from a reactive approach to the proactive prevention of disease, reducing long-term morbidity, mortality and healthcare costs.”
The researchers noted that a limitation to the study includes the fact that the algorithm was developed on an exclusively veteran population. They said they hope to conduct future studies in the general population and test whether the tool can assess the impact of lipid-lowering medications on CAC scores.
References
- Hagopian R, Strebel T, Bernatz S, et al. AI opportunistic coronary calcium screening at Veterans Affairs hospitals. N Engl J Med AI. 2025;2(6).
- EurekAlert. AI detects hidden heart disease using existing scans stored in patient records. June 16, 2025. Accessed June 17, 2025. www.eurekalert.org/news-releases/1087629