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Article

Assessing the robustness of an artificial intelligence segmentation model for quantitative cardiovascular magnetic resonance imaging across cardiac phenotypes.

The international journal of cardiovascular imaging - 1 Feb 2026

Saad Hadil, Ammann Clemens, Hadler Thomas, Bhoyroo Yashraj, Reisdorf Philine, Veit Jana, Chitiboi Teodora, Wetzl Jens, Geppert Christian, Schulz-Menger Jeanette

Abstract excerpt

PURPOSE: 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. METHODS: 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...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Share to Image Interpretation, Computer-Assisted

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Share to Ventricular Function, Left

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Source

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Identifiers

PMID: 41449266
DOI: 10.1007/s10554-025-03596-3
PMCID: PMC12909472

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

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