Artificial intelligence-based bi-ventricular systolic and diastolic volume, ejection fraction using non-contrast ECG-gated cardiac computed tomography.

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 pro

SUBMITTER: Chao MF 

PROVIDER: S-EPMC12631788 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature