How AI Analysis of Routine CT Scans Could Help Nashville Patients Identify Hidden Heart Disease Risk Early
Heart disease burden and the push for earlier detection
Cardiovascular disease remains the leading cause of death in the United States. Clinicians have long relied on traditional risk tools—such as blood pressure, cholesterol levels, diabetes status, smoking history, and family history—to estimate an individual’s likelihood of heart attack or stroke. Those measures, however, do not directly visualize disease inside the coronary arteries.
In Nashville and nationally, doctors are increasingly examining whether artificial intelligence can help find early, silent signs of coronary artery disease by extracting additional information from CT scans that were originally ordered for other medical reasons.
What AI is measuring on CT images
A central target for many AI tools is coronary artery calcium (CAC), a marker of atherosclerotic plaque visible on CT. CAC is typically summarized as an Agatston score and used to refine risk estimates, particularly for people who fall into intermediate-risk categories based on standard clinical calculators.
Traditionally, CAC scoring is performed using a dedicated, ECG-gated cardiac CT designed to minimize motion. Newer AI approaches attempt “opportunistic” CAC scoring on non-gated chest CT scans—such as those obtained for lung cancer screening or evaluation of pulmonary symptoms—potentially flagging patients whose scans already contain evidence of coronary calcification that might otherwise go unreported.
Dedicated CAC tests are optimized for cardiac motion control.
Opportunistic approaches aim to repurpose existing CT imaging, reducing the need for an additional scan in some cases.
AI systems may also quantify other imaging-derived markers, such as body composition features linked to cardiovascular risk.
Evidence and technical limits
Published research indicates AI can identify CAC on routine CT scans with performance that may support population-level screening workflows, particularly as health systems process large volumes of imaging. At the same time, peer-reviewed evaluations have reported clinically meaningful limitations on non-gated scans, including motion artifacts and vessel-specific errors that can affect risk-category assignment. These findings have led many experts to frame AI-based opportunistic scoring as a triage and detection tool rather than a replacement for dedicated cardiac imaging when precise quantification is required.
Clinically, the practical value of opportunistic CAC detection is often in identifying patients who may benefit from follow-up evaluation and risk-reduction discussions, rather than delivering a definitive diagnosis from a non-cardiac scan.
What this could mean for patients in Nashville
If deployed responsibly, AI-enabled review of routine CT scans could expand the number of people alerted to possible coronary artery disease before symptoms occur. In practice, a flagged finding would typically prompt clinician review, confirmation of the imaging interpretation, and a discussion of next steps—often including risk-factor optimization, consideration of guideline-based preventive therapies, and, when appropriate, additional testing tailored to the patient’s clinical picture.
Clinicians emphasize that imaging-derived risk signals must be integrated with the full medical context. CT-based findings do not replace evaluation of symptoms such as chest pain or shortness of breath, and AI outputs require oversight, quality controls, and clear follow-up pathways to avoid missed disease, unnecessary anxiety, or avoidable downstream testing.
Key takeaways
AI can help detect coronary calcium on CT scans that were not originally intended for heart evaluation.
Opportunistic screening may broaden early detection, but non-gated CT introduces technical challenges that can affect accuracy.
Clinical follow-up, radiologist oversight, and integration with standard risk factors remain essential.