biostudies-literatureUnknown42(2)Saad HCharité - Universitätsmedizin Berlin<h4>Purpose</h4>To introduce an artificial intelligence-based cardiovascular magnetic resonance segmentation algorithm (Nick) for automated quantification of function and parametric mapping across cardiac phenotypes reflecting clinical routine.<h4>Methods</h4>Nick was compared to manual gold standard (GS) segmentations in 359 multi-centre cases at 1.5T and 3T, consisting of 104 healthy individuals and 255 diseased patients with various cardiac phenotypes. Left and right ventricular (LV, RV) volumes and LV mass (LVM) were derived from short-axis segmentations. For parametric mapping, the LV myocardium was segmented to quantify T1 and T2 relaxation times. Statistical analysis comprised mean differences, correlation coefficients (R²), Bland-Altman analysis, tolerance range assessments, and paThe international journal of cardiovascular imaging343-357https://www.ebi.ac.uk/biostudies/studies/S-EPMC12909472biostudies-literatureAssessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypes.PMC12909472Wetzl JGeppert CHadler TVeit JReisdorf PBhoyroo YSchulz-Menger JAmmann CChitiboi TSaad HfalseAssessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypes.<h4>Purpose</h4>To introduce an artificial intelligence-based cardiovascular magnetic resonance segmentation algorithm (Nick) for automated quantification of function and parametric mapping across cardiac phenotypes reflecting clinical routine.<h4>Methods</h4>Nick was compared to manual gold standard (GS) segmentations in 359 multi-centre cases at 1.5T and 3T, consisting of 104 healthy individuals and 255 diseased patients with various cardiac phenotypes. Left and right ventricular (LV, RV) volumes and LV mass (LVM) were derived from short-axis segmentations. For parametric mapping, the LV myocardium was segmented to quantify T1 and T2 relaxation times. Statistical analysis comprised mean differences, correlation coefficients (R²), Bland-Altman analysis, tolerance range assessments, and pa2026-01-01T00:00:00Z2026 Feb2026-07-16T03:19:33.362Z2026-07-09T10:31:43.477ZS-EPMC129094724144926610.1007/s10554-025-03596-3