ArticleEuropean heart journal open2022
Left ventricular assessment with artificial intelligence increases the diagnostic accuracy of stress echocardiography.
Article in European heart journal open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Deep Learning for Cardiac Wall Motion Analysis: A Review of Methods, Challenges, and Clinical Applications.Annals of biomedical engineering · 2026Review
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- Charting the Current Landscape and Future Prospects of Cancer Therapy-Related Cardiovascular Toxicity in Cancer Survivors: From Bench to Bedside.Drug design, development and therapy · 2026Review
- Artificial intelligence implementation in automated heart chambers quantification during pharmacological stress echocardiography.European heart journal. Digital health · 2026Article
- Acute alterations in blood lactate in the setting of transient stress induced myocardial ischaemia.Experimental physiology · 2026Article
- Enhancement of Stress ECG Performance with Machine Learning: A Single-Center Study.JACC. Advances · 2025Article
- Artificial Intelligence in Coronary Artery Interventions: Preprocedural Planning and Procedural Assistance.Journal of the Society for Cardiovascular Angiography & Interventions · 2025Review
- Artificial Intelligence Applications in Cardio-Oncology: A Comprehensive Review.Current cardiology reports · 2025Review
- Evaluating artificial intelligence-driven stress echocardiography analysis system (EASE study): A mixed method study.BMJ open · 2024Article
- Assessment of an Artificial Intelligence Tool for Estimating Left Ventricular Ejection Fraction in Echocardiograms from Apical and Parasternal Long-Axis Views.Diagnostics (Basel, Switzerland) · 2024Article
- Multimodal Cardiac Imaging Revisited by Artificial Intelligence: An Innovative Way of Assessment or Just an Aid?Cureus · 2024Review
- Machine learning in cardiac stress test interpretation: a systematic review.European heart journal. Digital health · 2024Review
- Validation of machine learning models for estimation of left ventricular ejection fraction on point-of-care ultrasound: insights on features that impact performance.Echo research and practice · 2024Article
- The Role of Non-Invasive Multimodality Imaging in Chronic Coronary Syndrome: Anatomical and Functional Pathways.Diagnostics (Basel, Switzerland) · 2023Review
- Emerging Visual Language Models in Analysis of Echocardiography, Can They Solve the Challenges of Complex Congenital Heart Disease Echocardiography?Journal of cardiovascular echographyReview
Corrections and comments
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Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: To evaluate whether left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), automatically calculated by artificial intelligence (AI), increases the diagnostic performance of stress echocardiography (SE) for coronary artery disease (CAD) detection. Methods and results: SEs from 512 participants who underwent a clinically indicated SE (with or without contrast) for the evaluation of CAD from seven hospitals in the UK and US were studied. Visual wall motion scoring (WMS) was performed to identify inducible ischaemia. In addition, SE images at rest and stress underwent AI contouring for automated calculation of AI-LVEF and AI-GLS (apical two and four chamber images only) with Ultromics EchoGo Core 1.0. Receiver operator characteristic curves and multivariable risk models were used to assess accuracy for identification of participants subsequently found to have CAD on angiography. Participants with significant CAD were more likely to have abnormal WMS, AI-LVEF, and AI-GLS values at rest and stress (all Conclusion: AI calculation of LVEF and GLS by contouring of contrast-enhanced and unenhanced SEs at rest and stress is feasible and independently improves the identification of obstructive CAD beyond conventional WMSI.
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