ArticleJournal of cardiovascular imaging2023
Echocardiography Core Laboratory Validation of a Novel Vendor-Independent Web-Based Software for the Assessment of Left Ventricular Global Longitudinal Strain.
Article in Journal of cardiovascular imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence-assisted left ventricular global longitudinal strain assessment in patients with acute myocardial infarction: a RESUS-AMI trial sub-analysis.The international journal of cardiovascular imaging · 2025Trial
- Speckle-Tracking Echocardiography in Dogs: Evaluating Imaging Parameters and Methodological Variability in Global Longitudinal Strain Assessment.Animals : an open access journal from MDPI · 2025Article
- Comprehensive risk profiling and long-term cardiovascular toxicity in HER2-positive breast cancer patients treated with trastuzumab.Frontiers in oncology · 2025Article
- The role of artificial intelligence in standardizing global longitudinal strain measurements in echocardiography.European heart journal. Imaging methods and practice · 2024Article
- Artificial intelligence-derived left ventricular strain in echocardiography in patients treated with chemotherapy.The international journal of cardiovascular imaging · 2024Article
- Enhancing cardiology imaging: usability and implications of aortic annulus sizing software in transcatheter aortic valve replacement planning.Journal of cardiovascular imaging · 2024Article
- Path to Accurate and Universal Strain Measurement: Insights From a Validation Study.Journal of cardiovascular imaging · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
backgroundGlobal longitudinal strain (GLS) is an accurate and reproducible parameter of left ventricular (LV) systolic function which has shown meaningful prognostic value. Fast, user-friendly, and accurate tools are required for its widespread implementation. We aim to compare a novel web-based tool with two established algorithms for strain analysis and test its reproducibility.
methodsThirty echocardiographic datasets with focused LV acquisitions were analyzed using three different semi-automated endocardial GLS algorithms by two readers. Analyses were repeated by one reader for the purpose of intra-observer variability. CAAS Qardia (Pie Medical Imaging) was compared with 2DCPA and AutoLV (TomTec).
resultsMean GLS values were -15.0 ± 3.5% from Qardia, -15.3 ± 4.0% from 2DCPA, and -15.2 ± 3.8% from AutoLV. Mean GLS between Qardia and 2DCPA were not statistically different (p = 0.359), with a bias of -0.3%, limits of agreement (LOA) of 3.7%, and an intra-class correlation coefficient (ICC) of 0.88. Mean GLS between Qardia and AutoLV were not statistically different (p = 0.637), with a bias of -0.2%, LOA of 3.4%, and an ICC of 0.89. The coefficient of variation (CV) for intra-observer variability was 4.4% for Qardia, 8.4% 2DCPA, and 7.7% AutoLV. The CV for inter-observer variability was 4.5%, 8.1%, and 8.0%, respectively.
conclusionsIn echocardiographic datasets of good image quality analyzed at an independent core laboratory using a standardized annotation method, a novel web-based tool for GLS analysis showed consistent results when compared with two algorithms of an established platform. Moreover, inter- and intra-observer reproducibility results were excellent.
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