ReviewFrontiers in cardiovascular medicine2021
Machine Learning Augmented Echocardiography for Diastolic Function Assessment.
Review in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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
11 citing papers in PubMed, 29 citations in OpenAlex.
- Translating Mechanistic Insights Into Action and Revealing New Pathways: Machine Learning Approaches in Heart Failure With Preserved Ejection Fraction.Journal of the American Heart Association · 2026Review
- Left atrial strain in mitral valve disease: a comparative analysis of regurgitation and stenosis.BMC cardiovascular disorders · 2026Article
- Inter-machine harmonization of multicenter echocardiographic images for improvement of left ventricular ejection fraction prediction model.Scientific reports · 2025Article
- Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review.Journal of clinical medicine · 2025Review
- Applications of ChatGPT in Heart Failure Prevention, Diagnosis, Management, and Research: A Narrative Review.Diagnostics (Basel, Switzerland) · 2024Review
- Revolutionizing Cardiac Imaging: A Scoping Review of Artificial Intelligence in Echocardiography, CTA, and Cardiac MRI.Journal of imaging · 2024Article
- Artificial intelligence-enhanced automation of left ventricular diastolic assessment: a pilot study for feasibility, diagnostic validation, and outcome prediction.Cardiovascular diagnosis and therapy · 2024Article
- Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure.NPJ digital medicine · 2024Article
- Left ventricular to left arial volume ratio in the assessment of filling pressure in patients with dyspnoea and preserved ejection fraction.Frontiers in cardiovascular medicine · 2024Article
- The Role of Artificial Intelligence in Echocardiography.Journal of imaging · 2023Review
- Left Ventricular Segmental Strain Identifies Unique Myocardial Deformation Patterns After Intrinsic and Extrinsic Stressors in Mice.Ultrasound in medicine & biology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction-a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical guidelines advise using echocardiography measures to determine the grade of diastolic function. However, the current process has suboptimal accuracy, regular indeterminate classifications and is susceptible to confounding from comorbidities. Advances in artificial intelligence in recent years have created revolutionary ways to evaluate and integrate large quantities of cardiology data. Imaging is an area of particular strength for the sub-field of machine-learning, with evidence that trained algorithms can accurately discern cardiac structures, reliably estimate chamber volumes, and output systolic function metrics from echocardiographic images. In this review, we present the emerging field of machine-learning based echocardiographic diastolic function assessment. We summarise how machine-learning has made use of diastolic parameters to accurately differentiate pathology, to identify novel phenotypes within diastolic disease, and to grade diastolic function. Perspectives are given about how these innovations could be used to augment clinical practice, whilst areas for future investigation are identified.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.