********************Mayo Clinic AI spots hidden heart attack risk from pericardial fat in routine CAC scans********************
Key Questions
What did the Mayo Clinic AI study on pericardial fat reveal?
The AI analyzed pericardial fat from routine CAC scans in 12,000 patients over 16 years, identifying it as an independent predictor of heart disease risk beyond traditional risks and CAC score. It enhances risk stratification for borderline or low-risk individuals at no extra cost.
How does the AI measure pericardial fat?
The tool uses AI to quantify pericardial fat volume directly from standard non-contrast CAC CT scans. This scalable method integrates with EHRs and bolsters PREVENT/CAC staging without additional imaging.
Why is pericardial fat important for heart attack risk prediction?
Pericardial fat is metabolically active and promotes inflammation/atherosclerosis around the heart. The study showed it refines long-term CVD risk assessment in asymptomatic adults, complementing CAC.
How does this AI tool compare to retinal AI for risk prediction?
Mayo’s pericardial fat AI uses CAC scans for scalable cardiac risk assessment, while retinal AI from fundus images predicts CVD in diabetes/equity contexts. Both support non-invasive, AI-enhanced screening aligned with ACC.26 imaging advances.
What are the clinical implications of this technology?
It improves precision for low-risk patients, supports new coverage for CAC, and integrates with tools like Cleerly/Smidt at ACC.26. This could enhance equity in DM populations via complementary retinal AI.
Mayo AI-pericardial fat from CAC: 12k pts/16yr FU indep predictor atop risks/CAC, no extra cost. Bolsters PREVENT/CAC staging for borderline/low-risk; scalable EHR integration aligns with ACC.26 imaging/AI (Smidt/Cleerly); retinal AI fundus complements for DM/equity; new coverage reinforces.