Trial reportThe international journal of cardiovascular imaging2025
Artificial intelligence-assisted left ventricular global longitudinal strain assessment in patients with acute myocardial infarction: a RESUS-AMI trial sub-analysis.
Trial report in The international journal of cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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
1 citing paper in PubMed.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe aim of this sub-analysis of the RESUS-AMI trial was to evaluate the correlation of artificial intelligence (AI)-assisted echocardiographic global longitudinal strain (GLS) assessments with infarct size, left ventricular ejection fraction (LVEF) and volumes from cardiac magnetic resonance (CMR) imaging, in patients undergoing primary percutaneous coronary intervention for ST-elevation myocardial infarction. The reproducibility of GLS and other echocardiographic parameters derived with the AI-assisted software were also assessed.
methodsThis is a post-hoc imaging sub-analysis of the RESUS-AMI trial. Echocardiographic LVEF, volumes and GLS were measured with AI-assisted software (CAAS Qardia 2.0) using automated and semi-automated methods. The CMR LVEF, LV dimensions and infarct size were obtained from a CMR core lab with an off-line workstation (CAAS MRV 4.1).
resultsIn total 169 echocardiograms were analysed and the GLS showed moderate correlation with the CMR infarct size (r = 0.58 automated and 0.64 semi-automated, both p < 0.001) and LVEF (r=-0.63 automated and - 0.65 semi-automated, both p < 0.001) from 81 CMR recordings. GLS also showed moderate correlation with the LVEF (r= -0.51 automated and - 0.67 semi-automated, both p < 0.001) from echocardiography. The inter-observer reproducibility was excellent in GLS from both the automated (intraclass correlation (ICC) = 0.94, bias = 0.08, limit of agreement (LOA) = 1.75) and semi-automated analysis (ICC = 0.93, bias=-0.68, LOA = 1.44). The intra-observer reproducibility was excellent in all echocardiographic measurements.
conclusionGLS derived from the AI-assisted software (automated or semi-automated) could be used as a marker of LV systolic function as it correlates well the infarct size and LVEF assessed with CMR and LVEF with echocardiography.
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Registered trials
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