ArticleEuropean heart journal. Imaging methods and practice2025
Artificial intelligence-based bi-ventricular systolic and diastolic volume, ejection fraction using non-contrast ECG-gated cardiac computed tomography.
Article in European heart journal. Imaging methods and practice, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Calculation of Ejection Fraction Using Cardiac Computed Tomography: Clinical Evolution, Reliability, and Technological Challenges-A Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
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9 authors.
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Abstract
Aims: Ejection fraction (EF) and end-systolic volume (ESV) are prognostic markers in cardiovascular disease. While MRI provides accurate assessments, its cost limits widespread use. Non-contrast cardiac CT (NCCT), used for coronary artery disease screening, may offer additional functional information. To evaluate the accuracy of AI-derived ventricular volumes and EF from NCCT compared with contrast cardiac CT (CCT) and MRI. Methods and results: This single center study included 205 patients who underwent cardiac CT for valve planning, divided into retrospective and prospective cohorts. A validated AI algorithm was applied to low-dose NCCT images at end-diastole and end-systole. Right (RV) and left ventricles (LV) volumes and their EFs were compared with CCT and MRI. In the prospective cohort (49 women, 53 men; mean age 73.9 ± 10.3 years), NCCT correlated strongly with CCT for LVEDV (152 mL; -14.2% relative difference; Conclusion: AI-derived ventricular volumes from NCCT show moderate to strong correlations, but EF is underestimated. The derived EF can be a screening tool to rule out significant ventricular dysfunction.
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