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Updated: Aug 7, 2026
Semi-Automatic Graphical Tool for Measuring Coronary Artery Spatially Weighted Calcium Score from Gated Cardiac Computed Tomography Images
Published on: September 22, 2023
Heart Failure Outcome Prediction Using Artificial Intelligence-enabled Coronary Artery Calcium CT Chamber Volumetry
Eshan Momin1, Jaret Barr1, Gabrielle Gershon1
1Department of Radiology and Imaging Sciences, Emory University, 101 Woodruff Cir, Ste 308A, Atlanta, GA 30322.
Insights
Artificial intelligence (AI) analysis of cardiac chamber volumes from coronary artery calcium (CAC) CT scans significantly improves heart failure (HF) risk prediction compared to existing methods. This deep learning approach enhances early detection for better patient outcomes.
Area of Science:
- Cardiology
- Radiology
- Artificial Intelligence
Background:
- Coronary artery calcium (CAC) scoring is used for cardiovascular risk assessment.
- Predicting cardiovascular disease events-Heart Failure (PREVENT-HF) score is a clinical tool for heart failure risk stratification.
- Novel methods are needed to improve the accuracy of heart failure risk prediction.
Purpose of the Study:
- To evaluate if artificial intelligence (AI)-derived cardiac chamber volumetry from CAC CT scans can enhance heart failure (HF) risk prediction.
- To compare the predictive performance of AI-derived volumetry against traditional CAC scoring and the PREVENT-HF score.
Main Methods:
- Retrospective analysis of 5892 asymptomatic patients who underwent CAC CT scans.
- AI model used to calculate cardiac chamber volumes (left atrial, left ventricular, right atrial, left ventricular myocardial).
- Cox proportional hazard regression and time-dependent AUCs used to assess HF risk prediction at 3, 5, 8, and 10 years.
Main Results:
- Larger cardiac chamber volumes were independently associated with increased HF risk (P < .001).
- AI-derived chamber volumetry combined with CAC score and PREVENT-HF significantly outperformed PREVENT-HF alone (AUC 0.80 vs 0.76) and CAC score alone (AUC 0.80 vs 0.70) for 10-year HF risk prediction.
- Diastolic chamber volumes showed a stronger independent association with HF risk than systolic volumes.
Conclusions:
- AI-derived cardiac chamber volumetry from CAC CT scans offers improved heart failure risk prediction.
- This AI-driven approach provides incremental value beyond traditional CAC scoring and PREVENT-HF.
- Deep learning analysis of CT scans holds promise for enhanced cardiovascular risk stratification.
Abstract:
Purpose To evaluate whether artificial intelligence (AI)-derived chamber volumetry from coronary artery calcium (CAC) CT improves heart failure (HF) risk prediction. Materials and Methods This retrospective study included asymptomatic patients without known cardiac disease undergoing CAC CT between 2010 and 2023. CAC CT images were analyzed using a validated AI model to calculate chamber volumes. HF events were identified based on International Classification of Diseases, Ninth and Tenth Revisions codes. Cox proportional hazard regression models incorporating chamber volumes, adjusted for Predicting Risk of cardiovascular disease EVENTs-Heart Failure (PREVENT-HF) score, were used to assess the association with HF. Time-dependent areas under the receiver operating characteristic curve (AUCs) at 3, 5, 8, and 10 years were calculated to compare the performance of volumetry, PREVENT-HF, CAC scoring, and their combination. Results A total of 5892 patients were included (mean age ± SD, 58.2 years ± 9.4; 3258 male). During a mean follow-up of 4 years ± 3, 377 patients (6.3%) developed HF. Larger left atrial, left ventricular, right atrial, and left ventricular myocardial volumes were associated with increased HF risk (P < .001). The composite, multivariable Cox regression model incorporating all chamber volumes, CAC score, and PREVENT-HF scores outperformed PREVENT-HF (AUC, 0.80 vs 0.76; ΔAUC, 0.04; P < .001) and CAC scores (AUC, 0.80 vs 0.70; ΔAUC, 0.10; P < .001) alone in predicting 10-year HF risk. A phase-volume interaction effect was identified, indicating that diastolic volumes were independently associated with higher HF risk than were systolic volumes (P < .001), adjusted for PREVENT-HF. Conclusion AI-derived cardiac chamber volumetry obtained from CAC CT improved HF risk prediction compared with PREVENT-HF or CAC scores alone. Keywords: Applications-CT, Deep Learning, Cardiac Supplemental material is available for this article. © RSNA, 2026.
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Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...