ReviewHeart (British Cardiac Society)2022
Artificial intelligence for the echocardiographic assessment of valvular heart disease.
Review in Heart (British Cardiac Society), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
What it found
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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
48 citing papers in PubMed.
- Article
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- DCFFNet: A New Dual-channel Cross-Feature Fusion Net for Evaluating the Degree of Aortic Valve Calcification Based on Echocardiographic Images.Journal of imaging informatics in medicine · 2026Article
- Agreement of AI-assisted semi-automated right ventricular function analysis using 3D transthoracic echocardiography with artificial intelligence.Journal of echocardiography · 2026Article
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Artificial Intelligence Across the Echocardiographic Workflow: A Narrative Review for Clinicians.Cureus · 2026Review
- A multi-view autoencoder architecture with self-adaptive feature recalibration and confidence-aware ensemble for heart disease classification.Scientific reports · 2026Article
- Multimodal Fusion of Echocardiogram Images and Electronic Medical Records for Heart Disease Screening: Retrospective Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Real-time deep learning interpretation of echocardiographic video for automated detection of anatomical features associated with tetralogy of fallot in pediatric patients : a feasibility study.Scientific reports · 2026Article
- Artificial Intelligence in Cardiovascular Medicine: A Giant Step in Personalized Medicine?Journal of personalized medicine · 2026Review
- Application of the New 2023 World Heart Federation Criteria to Rheumatic Heart Disease in the Setting of First Episode of Acute Rheumatic Fever: Predictors for Resolution.Pediatric cardiology · 2026Article
- Revolutionizing Heart Failure Management With Artificial Intelligence: A Narrative Review of Diagnostic, Prognostic, and Therapeutic Innovations.Health science reports · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Automated analysis of echocardiographic images: do different artificial intelligence tools provide concordant measurements?The international journal of cardiovascular imaging · 2026Article
- AI-assisted multimodal assessment for right ventricular function from echocardiography predicts mortality in patients with pulmonary hypertension and right heart failure.Scientific reports · 2026Article
- Asymptomatic Aortic Regurgitation: Evolving Imaging Markers and Contemporary Intervention Strategies.Journal of clinical medicine · 2026Review
- Echo-Vision-FM: a pre-training and fine-tuning framework for echocardiogram video vision foundation model.Nature communications · 2025Article
- Epidemiology, pathophysiology, diagnosis and management of atrial functional mitral regurgitation: An expert opinion paper.ESC heart failure · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developments in artificial intelligence (AI) have led to an explosion of studies exploring its application to cardiovascular medicine. Due to the need for training and expertise, one area where AI could be impactful would be in the diagnosis and management of valvular heart disease. This is because AI can be applied to the multitude of data generated from clinical assessments, imaging and biochemical testing during the care of the patient. In the area of valvular heart disease, the focus of AI has been on the echocardiographic assessment and phenotyping of patient populations to identify high-risk groups. AI can assist image acquisition, view identification for review, and segmentation of valve and cardiac structures for automated analysis. Using image recognition algorithms, aortic and mitral valve disease states have been directly detected from the images themselves. Measurements obtained during echocardiographic valvular assessment have been integrated with other clinical data to identify novel aortic valve disease subgroups and describe new predictors of aortic valve disease progression. In the future, AI could integrate echocardiographic parameters with other clinical data for precision medical management of patients with valvular heart disease.
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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.