ReviewEuropean heart journal. Digital health2026
Reproducibility of echocardiographic measurements of left ventricular systolic function: a systematic review and meta-analysis comparing artificial intelligence and clinician estimates.
Review in European heart journal. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Echocardiography underpins the diagnosis and management of cardiovascular disease, yet measurement variability can influence treatment decisions. Artificial intelligence (AI) may standardize interpretation, but its reproducibility and clinical impact require systematic evaluation. To compare the reproducibility of AI-derived and clinician-derived measurements of left ventricular (LV) systolic function, specifically global longitudinal strain (GLS) and ejection fraction (EF), in adults. We searched Medline, Embase, Web of Science, and CENTRAL from inception to May 2025 for peer-reviewed studies assessing the reproducibility of AI-derived EF and/or GLS from two-dimensional (2D) or three-dimensional (3D) transthoracic echocardiography. Reporting quality was assessed with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Random-effects meta-analyses of intraclass correlation coefficients (ICCs) and Bland-Altman plots compared reproducibility of AI- and clinician-derived measures Nineteen studies (17 984 participants; mean age 59 ± 8 years, 52.8% male) were included. Mean CLAIM adherence was 72.9%. Pooled ICCs demonstrated high reproducibility for both AI- and clinician-derived EF and GLS. Bland-Altman analyses showed limits of agreement of -13.4% to +12.7% for 2D EF and -4.3% to +2.3% for 2D GLS. 3D EF was slightly better, showing pooled limits of agreement of 11.26-12.61%. The pooled mean absolute differences (MAD) were 5.17% for 2D EF, 5.27% for 3D EF, and 1.32% for 2D GLS. AI-derived GLS and 3D EF achieve reproducibility comparable to, or exceeding, clinicians' estimates. However, the limits of agreement between clinician and AI estimates are sufficiently wide that reclassification is possible around key thresholds, which could affect patient management decisions. Large-scale, real-world validation remains essential to confirm generalizability.
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