ArticleJournal of cardiovascular computed tomography
AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CAC
Article in Journal of cardiovascular computed tomography. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 17 citations in OpenAlex.
- Comparing incidence of heart failure in individuals with enlarged cardiac chambers versus diabetes.American journal of preventive cardiology · 2026Article
- AI-enabled left atrial volume quantification from CAC CT: comparison with echocardiography.The international journal of cardiovascular imaging · 2026Article
- Calculation of Ejection Fraction Using Cardiac Computed Tomography: Clinical Evolution, Reliability, and Technological Challenges-A Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- AI-CVD-HF: A heart failure risk prediction model based on coronary artery calcium scans compared with PREVENT-HF.American journal of preventive cardiology · 2026Article
- Article
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Plasma proteomics and coronary artery calcium score: synergistic, concordant and contrasting predictions of cardiovascular outcomes in The Multi-Ethnic Study of Atherosclerosis.medRxiv : the preprint server for health sciences · 2026Article
- Detection of Biventricular Volume Increase in Overweight and Obese Individuals Using a Novel Index of the "Standard Human"-A Single-Center, Non-Contrast-Enhanced Cardiac CT Study.Journal of clinical medicine · 2026Article
- Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.Radiology. Cardiothoracic imaging · 2026Article
- Review
- Opportunistic AI-derived adiposity measures from coronary artery calcium scans predict new-onset type 2 diabetes in adults without obesity or hyperglycemia: insights from the AI-CVD study within MESA.Diabetology & metabolic syndrome · 2025Article
- A Novel Concept of the "Standard Human" in the Assessment of Individual Total Heart Size: Lessons from Non-Contrast-Enhanced Cardiac CT Examinations.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Diagnosis of Heart Failure.Journal of the American Heart Association · 2025Review
- AI-enabled opportunistic measurement of liver steatosis in coronary artery calcium scans predicts cardiovascular events and all-cause mortality: an AI-CVD study within the Multi-Ethnic Study of Atherosclerosis (MESA).BMJ open diabetes research & care · 2025Article
- Recurrence and non-improvement of European Heart Rhythm Association symptom scores after atrial fibrillation ablation: the role of left atrial fractal dimension.Quantitative imaging in medicine and surgery · 2025Article
- Artificial intelligence applied to coronary artery calcium scans (AI-CAC) significantly improves cardiovascular events prediction.NPJ digital medicine · 2024Article
- AI-Enabled CT Cardiac Chamber Volumetry Predicts Atrial Fibrillation and Stroke Comparable to MRI.JACC. Advances · 2024Article
- Article
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Authors and funding
13 authors at 6 institutions in 1 country.
Funding
Abstract
introductionCoronary artery calcium (CAC) scans contain useful information beyond the Agatston CAC score that is not currently reported. We recently reported that artificial intelligence (AI)-enabled cardiac chambers volumetry in CAC scans (AI-CAC™) predicted incident atrial fibrillation in the Multi-Ethnic Study of Atherosclerosis (MESA). In this study, we investigated the performance of AI-CAC cardiac chambers for prediction of incident heart failure (HF).
methodsWe applied AI-CAC to 5750 CAC scans of asymptomatic individuals (52% female, White 40%, Black 26%, Hispanic 22% Chinese 12%) free of known cardiovascular disease at the MESA baseline examination (2000-2002). We used the 15-year outcomes data and compared the time-dependent area under the curve (AUC) of AI-CAC volumetry versus NT-proBNP, Agatston score, and 9 known clinical risk factors (age, gender, diabetes, current smoking, hypertension medication, systolic and diastolic blood pressure, LDL, HDL for predicting incident HF over 15 years.
resultsOver 15 years of follow-up, 256 HF events accrued. The time-dependent AUC [95% CI] at 15 years for predicting HF with AI-CAC all chambers volumetry (0.86 [0.82,0.91]) was significantly higher than NT-proBNP (0.74 [0.69, 0.77]) and Agatston score (0.71 [0.68, 0.78]) (p < 0.0001), and comparable to clinical risk factors (0.85, p = 0.4141). Category-free Net Reclassification Index (NRI) [95% CI] adding AI-CAC LV significantly improved on clinical risk factors (0.32 [0.16,0.41]), NT-proBNP (0.46 [0.33,0.58]), and Agatston score (0.71 [0.57,0.81]) for HF prediction at 15 years (p < 0.0001).
conclusionAI-CAC volumetry significantly outperformed NT-proBNP and the Agatston CAC score, and significantly improved the AUC and category-free NRI of clinical risk factors for incident HF prediction.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.