In a groundbreaking advancement that could redefine cardiovascular risk assessment, researchers at Mayo Clinic have harnessed the power of artificial intelligence (AI) to markedly enhance the predictive accuracy of coronary artery scans. This innovative approach capitalizes on existing clinical imaging technology to provide a deeper understanding of heart disease risk, an area that remains a leading global health challenge. The study, presented at the 2026 American College of Cardiology Scientific Session and published in the American Journal of Preventive Cardiology, stands out for its ambitious long-term follow-up and integration of AI with well-established risk models.
Traditional cardiovascular risk prediction has relied heavily on a combination of clinical factors such as age, sex, blood pressure, cholesterol levels, and diabetes status, coupled with imaging techniques like coronary artery calcium (CAC) scoring. CAC scoring quantifies the extent of calcified plaque deposits within coronary arteries and has been a staple in routine cardiovascular evaluations for many years. Despite its utility, CAC has limitations, particularly in stratifying risk among patients who fall into borderline or intermediate categories. This is where the Mayo Clinic study’s innovation shines, by augmenting CAC assessments with AI-driven analysis of pericardial adipose tissue—the fat surrounding the heart.
The research team applied deep learning algorithms to electrocardiogram-gated cardiac computed tomography (CT) scans of nearly 12,000 adults, performed over a span of approximately 16 years. Unlike traditional manual measurements, AI enabled rapid, automated quantification of pericardial fat volume, ensuring consistency and reproducibility at scale. This task, which had previously been cumbersome and variable, was revolutionized by AI’s capacity to sift through imaging data with unprecedented precision, extracting nuanced information beyond simple calcium scoring.
Crucially, the volume of pericardial fat emerged as an independent predictor of cardiovascular events, including heart attacks and strokes, even after adjusting for established risk factors and CAC scores. This finding challenges the conventional understanding that focuses predominantly on coronary calcification and highlights the metabolic and inflammatory nuances that pericardial adipose tissue may signify. The accumulation of fat around the heart is increasingly recognized as a dynamic factor influencing coronary artery disease through local inflammatory processes and its impact on myocardial function.
Integration of pericardial fat measurements with standard risk equations like the American Heart Association’s PREVENT model considerably improved the accuracy of long-term cardiovascular risk predictions. The combined model demonstrated heightened discriminatory power especially among patients stratified as low or intermediate risk based on traditional assessments. This precision medicine approach offers clinicians a powerful new tool to tailor preventative strategies, potentially initiating earlier interventions for those who may otherwise be overlooked.
One of the most compelling aspects of the study is that it leverages imaging already performed as part of routine clinical care, eliminating the need for additional tests, radiation exposure, or costs. Coronary CT scans, being widely adopted in clinical settings, now serve a dual purpose: traditional calcium scoring and AI-enhanced quantification of cardiac fat. This novel methodology is not only practical but scalable, paving the way for wide dissemination and immediate clinical impact.
The lead researcher, Zahra Esmaeili, emphasized the transformative potential of this approach. The automatic and precise measurement of pericardial fat can help augment the clinical decision-making process where ambiguities exist, particularly for patients on the threshold of risk categories. By delivering more detailed patient-specific risk profiles, healthcare providers can advance towards more personalized and effective cardiovascular disease prevention.
Senior author Francisco Lopez-Jimenez, director of the AI in Cardiology program at Mayo Clinic, underscored the synergy between cutting-edge AI techniques and traditional cardiovascular diagnostics. This collaboration promises to revolutionize screening and preventative cardiology by enabling clinicians to identify subtle yet meaningful indicators of disease earlier in the pathological trajectory, ultimately reducing the burden of cardiovascular morbidity and mortality.
Throughout the longitudinal study, nearly 10% of participants developed cardiovascular disease, reinforcing the persistent threat imposed by heart disease worldwide. Notably, individuals with the highest volumes of pericardial fat faced elevated risks regardless of their coronary calcium burden, suggesting that pericardial fat quantification captures distinct biological signals with profound prognostic importance.
The study not only augments the existing scientific knowledge on cardiac adiposity’s role in coronary artery disease but also presents a clear avenue for translation into clinical practice. Future research is aimed at refining algorithms, validating findings across diverse populations, and integrating this approach into routine workflows. Determining how best to incorporate these measurements into clinical guidelines will be a key focus, as will exploring therapeutic implications and whether interventions targeting pericardial fat reduction can improve cardiovascular outcomes.
In essence, Mayo Clinic’s AI-driven quantification of pericardial adipose tissue signifies a paradigm shift from traditional risk models towards a more mechanistic and individualized understanding of cardiovascular risk. As heart disease continues to impose a heavy toll globally, innovations like this provide hope for more effective disease prevention through earlier detection and personalized care strategies delivered seamlessly within existing healthcare frameworks.
This study exemplifies the burgeoning potential of AI in medicine, where sophisticated computational models unlock unprecedented insights from standard diagnostic tools. Such advances herald a new era where the amalgamation of technology and medicine transcends previous limitations, driving forward the promise of next-generation precision cardiovascular care.
Subject of Research: Artificial intelligence-enhanced cardiovascular risk prediction using pericardial adipose tissue quantification in coronary artery calcium scans.
Article Title: Deep learning-derived pericardial adipose tissue by electrocardiogram-gated cardiac computed tomography predicts cardiovascular events beyond coronary calcium score
News Publication Date: 24-Mar-2026
Web References:
– American Journal of Preventive Cardiology publication: https://www.sciencedirect.com/science/article/pii/S2666667726001431
– Mayo Clinic AI in Cardiology program: https://www.mayoclinic.org/departments-centers/ai-cardiology/overview/ovc-20486648
– 2026 American College of Cardiology Scientific Session: https://accscientificsession.acc.org/
References:
Esmaeili, Z., Lopez-Jimenez, F., et al. (2026). Deep learning-derived pericardial adipose tissue by electrocardiogram-gated computed tomography predicts cardiovascular events beyond coronary calcium. American Journal of Preventive Cardiology.
Image Credits: Not provided.
Keywords
Artificial intelligence, Cardiovascular disease, Coronary artery calcium scoring, Pericardial adipose tissue, Cardiac computed tomography, Risk prediction, Deep learning, Precision medicine, Preventive cardiology, AI in healthcare, Cardiac imaging, Long-term cardiovascular risk
