ArticleFrontiers in cardiovascular medicine2024
Concordance of left ventricular volumes and function measurements between two human readers, a fully automated AI algorithm, and the 3D heart model.
Article in Frontiers in cardiovascular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Automated echocardiographic measurements for longitudinal monitoring of ATTR cardiomyopathy: agreement and repeatability analysis.The international journal of cardiovascular imaging · 2026Article
- Validation of a fully automated workflow for longitudinal strain using artificial intelligence.Journal of echocardiography · 2026Article
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Reproducibility of echocardiographic measurements of left ventricular systolic function: a systematic review and meta-analysis comparing artificial intelligence and clinician estimates.European heart journal. Digital health · 2026Review
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Association of echocardiographic findings with mortality: human assessment vs. automated deep learning analysis.European heart journal. Digital health · 2026Article
- Artificial intelligence implementation in automated heart chambers quantification during pharmacological stress echocardiography.European heart journal. Digital health · 2026Article
- Pre-Hospital Artificial Intelligence-Guided, Focused Echocardiography in Patients with Acute Chest Pain for Diagnosis of Acute Coronary Syndrome.Journal of clinical medicine · 2025Article
- Artificial intelligence diagnosis and heatmap agent for mitral valve prolapse using 3D cine echocardiography.iScience · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Echocardiography is essential in cardiovascular medicine for screening, diagnosis, and monitoring. Artificial intelligence (AI) has the potential to improve echocardiography by reducing variability and analysis time. While 3D echocardiography is becoming more accurate, 2D imaging still dominates clinical care. We aimed to evaluate agreement in measures of left ventricular (LV) volumes and function between human readers, a fully automated AI 2D algorithm, and the 3D Heart Model. Methods: A retrospective analysis was conducted on 109 patients who underwent 2D and 3D transthoracic echocardiography. LV end-diastolic and end-systolic volumes (LVEDV, LVESV) and ejection fraction (LVEF) were measured by two operators, a commercially available AI algorithm (US2ai), and the 3D Heart Model. Global longitudinal strain (GLS) was measured by the integrated semi-automated software and the AI algorithm. Outcomes included measures of agreement [bias, limit of agreement and Pearson's correlation (R)]. Results: For LV volume measurements, the AI algorithm was strongly correlated with the average of the human operators ( Conclusion: This study demonstrates AI-based echocardiography as a promising tool for accurately assessing LV volumes and LVEF in clinical practice. AI-based measures demonstrated a significantly lower inter-operator variability, thereby improving the consistency and reliability of these assessments. Moreover, AI may prove particularly effective for conducting retrospective bulk analyses, offering a valuable tool for comprehensive evaluations of past data.
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