Evidence map›Paper›PMID 42806181›Full record

ArticleThe international journal of cardiovascular imaging2026

AI-enabled left atrial volume quantification from CAC CT: comparison with echocardiography.

Eshan Momin, Jaret Barr, Gabrielle Gershon, Brian Zhou, Anurag Mehta, Saikiran Rapaka, Athira Jacob, Carlo N De Cecco, Marly van Assen

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Article in The international journal of cardiovascular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Eshan MominDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Jaret BarrDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Gabrielle GershonDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Brian ZhouDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Anurag MehtaDepartment of Medicine, Division of Cardiology, Emory University, Atlanta, GA, USA.
Saikiran RapakaDigital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA.
Athira JacobDigital Technology and Innovation, Siemens Healthineers, Princeton, NJ, USA.
Carlo N De CeccoDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA.
Marly van AssenDepartment of Radiology and Imaging Sciences, Emory University, Atlanta, GA, USA. marly.van.assen@emory.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Left atrial (LA) volume is a powerful prognostic factor in cardiovascular disease with emerging AI-driven quantification from non-contrast coronary artery calcium (CAC) CT. The accuracy and agreement of such volume quantification with echocardiography, however, is not well studied. This study compares LA volumes captured from CAC CT and transthoracic two-dimensional echocardiography (2DE). This retrospective cohort included 220 patients undergoing both echocardiography and CAC CT within a 1-year period. CAC CT images were analyzed using a deep learning-based autoencoder model to derive chamber volumes. LA volume measurements were compared using Pearson correlation and Bland-Altman plots. Sub analysis was performed based on CT cardiac phase, body mass index, and sex with LA volume indexed to body-surface area (BSA). Mean LA volume differed significantly between 2DE and CAC CT with volumes of 32.2 ± 13.7 mL/m2 and 45.7 ± 15.9 mL/m2 (P < 0.001), respectively. CAC CT-derived LA volume correlated strongly with 2DE-measured LA volume (r = 0.82, ICC = 0.57, P < 0.001) and substantially with clinical severity (Cohen's κ = 0.70, P < 0.001). 2D-echo systematically underestimated LA volume with CAC-CT, with bias of 13.5 mL/m2 [95% CI, 12.3-14.7]. Agreement remained consistent for patients with (r = 0.83, ICC = 0.66, P < 0.001) and without (r = 0.77, ICC = 0.62, P < 0.001) heart failure. Inter-modality agreement for chamber volumes remained robust across sex, BMI, cardiac phase, and vendor sub-analyses. AI-derived LA volume from CAC CT demonstrated strong correlations with 2DE measurements and severity regardless of cardiac phase, BMI, and sex. Despite systematic differences in absolute volume compared to 2DE, CAC CT revealed relative LA remodeling, supporting the use of CAC CT as a modality for chamber quantification.

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Artificial IntelligenceCardiac CTChamber VolumesEchocardiographyHeart failure

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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.