ArticleKorean circulation journal2024
An Artificial Intelligence-Based Automated Echocardiographic Analysis: Enhancing Efficiency and Prognostic Evaluation in Patients With Revascularized STEMI.
Article in Korean circulation journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Predictive Effect of Echocardiographic Assessment of Myocardial Efficiency on Martial Arts Performance.Journal of sports science & medicine · 2026Observational
- Myocardial Strain in Perioperative Medicine: A Practical Review for Anesthesiologists.Journal of clinical medicine · 2026Review
- Prognostic value of left atrial reservoir strain for major adverse cardiovascular events in coronary artery disease.Journal of echocardiography · 2026Review
- Fully automated artificial intelligence-based echocardiographic analysis substantially reduces workflow time while preserving measurement accuracy: a pilot study.Journal of cardiovascular imaging · 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
- Deep learning-based multi-view echocardiographic framework for comprehensive diagnosis of pericardial disease.European heart journal. Digital health · 2026Article
- Machine-learning-derived phenotypes of hypertensive patients using multidimensional clinical and echocardiographic data including strain imaging.European heart journal. Digital health · 2026Article
- Artificial intelligence implementation in automated heart chambers quantification during pharmacological stress echocardiography.European heart journal. Digital health · 2026Article
- Novel deep learning framework for simultaneous assessment of left ventricular mass and longitudinal strain: clinical feasibility and validation in patients with hypertrophic cardiomyopathy.Journal of echocardiography · 2025Article
- Left Atrial Reservoir Strain in Cardiovascular and Systemic Disease: Advances and Clinical Applications From Physiology to Practice.Reviews in cardiovascular medicine · 2025Review
- Utilizing deep learning for accurate assessment of aortic valve stenosis: case series for clinical applications.Journal of cardiovascular imaging · 2025Article
- AI-Based Automated Echocardiographic Analysis is Expected to Revolutionize Clinical Practice.Korean circulation journal · 2024Article
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Authors and funding
16 authors.
Funding
Abstract
BACKGROUND AND
objectivesAlthough various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI).
methodsThe AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI.
resultsThe AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81-0.92 and intraclass correlation coefficients ranging 0.74-0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements.
conclusionsOur fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.
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