ArticleJACC. Advances2024
An Artificial Intelligence Algorithm for Detection of Severe Aortic Stenosis: A Clinical Cohort Study.
Article in JACC. Advances, 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.
- Article
- 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
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Categories of Aortic Stenosis: What's New and the Clinical Implications.Medicina (Kaunas, Lithuania) · 2026Review
- Clinical Applications of Artificial Intelligence in Structural Heart Disease.Journal of the Society for Cardiovascular Angiography & Interventions · 2026Review
- Diagnosing Severe Low-Gradient vs Moderate Aortic Stenosis with Artificial Intelligence Based on Echocardiography Images.Journal of imaging informatics in medicine · 2026Article
- The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging.Current cardiology reports · 2026Review
- Incidence and severity of aortic stenosis according to machine learning predicted risk of atrial fibrillation.Scientific reports · 2025Article
- The Impact of Artificial Intelligence on Women's Cardiovascular Disease Care.Current cardiology reports · 2025Review
- Detection of severe aortic stenosis by clinicians versus artificial intelligence: A retrospective clinical cohort study.American heart journal plus : cardiology research and practice · 2024Article
- Navigating the Gray Zone: AI Decision Support to Identify Aortic Stenosis Severity.JACC. Advances · 2024Article
- AI in Cardiology: Improving Outcomes for All.JACC. Advances · 2024Article
- 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Identifying individuals with severe aortic stenosis (AS) at high risk of mortality remains challenging using current clinical imaging methods. Objectives: The purpose of this study was to evaluate an artificial intelligence decision support algorithm (AI-DSA) to augment the detection of severe AS within a well-resourced health care setting. Methods: Agnostic to clinical information, an AI-DSA trained to identify echocardiographic phenotype associated with an aortic valve area (AVA)<1 cm Results: Performance of AI-DSA to detect the phenotype associated with an AVA<1 cm Conclusions: Without relying on left ventricular outflow tract measurements, an AI-DSA used echocardiographic reports to reliably identify the phenotype of severe AS. These results suggest possible utility for this AI-DSA to enhance detection of severe AS individuals at risk for adverse outcomes.
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