ArticleJACC. Advances2024
AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR.
Article in JACC. Advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Revisiting Perfusion in the Forgotten Right Ventricle: Artificial Intelligence-Enhanced Quantification on PET/CT.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Opportunistic CT Attenuation Biomarkers of Anemia Are Associated With Impaired Myocardial Flow Reserve and Cardiovascular Outcomes.medRxiv : the preprint server for health sciences · 2026Article
- Left Ventricular Geometry Improves Prediction of Sex-Specific Post-TAVR Remodeling in Aortic Stenosis.medRxiv : the preprint server for health sciences · 2026Article
- Deep Learning-Derived Cardiac Chamber Volumes and Mass From PET/CT Attenuation Scans: Associations With Myocardial Flow Reserve and Heart Failure.Circulation. Cardiovascular imaging · 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
- AI-derived automated quantification of cardiac chambers and myocardium from non-contrast CT: Prediction of major adverse cardiovascular events in asymptomatic subjects.Atherosclerosis · 2025Article
- Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against Cardiac MR.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR.
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Registered trials
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