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. 2025 Oct:288:122-130.
doi: 10.1016/j.ahj.2025.04.019. Epub 2025 Apr 24.

Opportunistic assessment of abdominal aortic calcification using artificial intelligence (AI) predicts coronary artery disease and cardiovascular events

Jeffrey S Berger  1 , Chen Lyu  2 , Eduardo Iturrate  3 , Malte Westerhoff  4 , Soterios Gyftopoulos  5 , Bari Dane  5 , Judy Zhong  6 , Michael Recht  5 , Miriam A Bredella  7
Affiliations

Opportunistic assessment of abdominal aortic calcification using artificial intelligence (AI) predicts coronary artery disease and cardiovascular events

Jeffrey S Berger et al. Am Heart J. 2025 Oct.

Abstract

Background: Abdominal computed tomography (CT) is commonly performed in adults. Abdominal aortic calcification (AAC) can be visualized and quantified using artificial intelligence (AI) on CTs performed for other clinical purposes (opportunistic CT). We sought to investigate the value of AI-enabled AAC quantification as a predictor of coronary artery disease and its association with cardiovascular events.

Methods: A fully automated AI algorithm to quantify AAC from the diaphragm to aortic bifurcation using the Agatston score was retrospectively applied to a cohort of patient that underwent both noncontrast abdominal CT for routine clinical care and cardiac CT for coronary artery calcification (CAC) assessment. Subjects were followed for a median of 36 months for major adverse cardiovascular events (MACE, composite of death, myocardial infarction [MI], ischemic stroke, coronary revascularization) and major coronary events (MCE, MI or coronary revascularization). The 10-year Predicting Risk of cardiovascular disease EVENTs (PREVENT) cardiovascular risk score was calculated.

Results: Our cohort included 3599 patients (median age 61 years, 49% female, 73% white) with an evaluable abdominal and cardiac CT. There was a positive correlation between presence and severity of AAC and CAC (r = 0.56, P < .001). AAC showed excellent discriminatory power for detecting or ruling out any CAC (AUC for PREVENT risk score 0.701 [0.683-0.718]; AUC for PREVENT plus AAC 0.782 [0.767-0.797]; P < .001). There were 324 MACE, of which 246 were MCE. Following adjustment for the PREVENT score, the presence of AAC was associated with a significant risk of MACE (adjHR 2.26, 95% CI 1.67-3.07, P < .001) and MCE (adjHR 2.58, 95% CI 1.80-3.71, P < .001). A doubling of the AAC score resulted in an 11% increase in the risk of MACE and a 13% increase in the risk of MCE.

Conclusions: Using opportunistic abdominal CTs, assessment of AAC using a fully automated AI algorithm, predicted CAC and was independently associated with cardiovascular events. These data support the use of opportunistic imaging for cardiovascular risk assessment. Future studies should investigate whether opportunistic imaging can help guide appropriate cardiovascular prevention strategies.

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Conflict of interest statement

Declaration of competing interest MW: Dr. Westerhoff is an employee of Visage Imaging and shareholder of its parent company Promedicus Ltd. BD: Dr. Dane received speaker honorarium, research support, and travel from Siemens Healthineers.

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