ArticleJournal of cardiovascular computed tomography
AI-enabled left atrial 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 10 papers.
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10 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- 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
- 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
- Baseline atrial volume indices and major adverse cardiac events following thoracic radiotherapy.Frontiers in cardiovascular medicine · 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
- AI-enabled cardiac chambers volumetry in coronary artery calcium scans (AI-CACJournal of cardiovascular computed tomographyArticle
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Authors and funding
16 authors at 8 institutions in 2 countries.
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Abstract
backgroundCoronary artery calcium (CAC) scans contain actionable information beyond CAC scores that is not currently reported.
methodsWe have applied artificial intelligence-enabled automated cardiac chambers volumetry to CAC scans (AI-CAC
resultsAt 1,2,3,4, and 5 years follow-up 36, 77, 123, 182, and 236 cases of AF were identified, respectively. The AUC for AI-CAC LA volume was significantly higher than CHARGE-AF for Years 1, 2, and 3 (0.83 vs. 0.74, 0.84 vs. 0.80, and 0.81 vs. 0.78, respectively, all p < 0.05), but similar for Years 4 and 5, and significantly higher than NT-proBNP at Years 1-5 (all p < 0.01), but not for combined CHARGE-AF and NT-proBNP at any year. AI-CAC LA significantly improved the continuous Net Reclassification Index for prediction of AF over years 1-5 when added to CHARGE-AF Risk Score (0.60, 0.28, 0.32, 0.19, 0.24), and NT-proBNP (0.68, 0.44, 0.42, 0.30, 0.37) (all p < 0.01).
conclusionAI-CAC LA volume enabled prediction of AF as early as one year and significantly improved on risk classification of CHARGE-AF Risk Score and NT-proBNP.
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