ArticleOpen heart2023
Enhanced detection of severe aortic stenosis via artificial intelligence: a clinical cohort study.
Article in Open heart, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Unimodal to multimodal: a systematic review of predictive machine learning models for valvular heart diseases.Frontiers in cardiovascular medicine · 2026Pooled it
- Article
- Artificial intelligence in echocardiography: a position statement from the British Society of Echocardiography.Echo research and practice · 2026Review
- Diagnosing Severe Low-Gradient vs Moderate Aortic Stenosis with Artificial Intelligence Based on Echocardiography Images.Journal of imaging informatics in medicine · 2026Article
- Detecting Bicuspid Aortic Valve From Echocardiographic Reports Using Natural Language Processing: A Veterans Affairs Study.JACC. Advances · 2026Article
- Artificial intelligence enhanced contemporary pulmonary hypertension care.American heart journal plus : cardiology research and practice · 2025Review
- Deep learning-assisted aortic stenosis detection and grading based on multiview versus single-view echocardiography.Quantitative imaging in medicine and surgery · 2025Article
- Contemporary applications of artificial intelligence and machine learning in echocardiography.NPJ cardiovascular health · 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
- Article
- An Artificial Intelligence Algorithm for Detection of Severe Aortic Stenosis: A Clinical Cohort Study.JACC. Advances · 2024Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.medRxiv : the preprint server for health sciences · 2024Article
- Drawing First Blood: Biomarkers and TAVR Outcomes.JACC. Advances · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveWe developed an artificial intelligence decision support algorithm (AI-DSA) that uses routine echocardiographic measurements to identify severe aortic stenosis (AS) phenotypes associated with high mortality.
methods631 824 individuals with 1.08 million echocardiograms were randomly spilt into two groups. Data from 442 276 individuals (70%) entered a Mixture Density Network (MDN) model to train an AI-DSA to predict an aortic valve area <1 cm
resultsThe area under receiver operating characteristic curve for the AI-DSA to detect severe AS was 0.986 (95% CI 0.985 to 0.987) with 4622/88 199 (5.2%) individuals (79.0±11.9 years, 52.4% women) categorised as 'high-probability' severe AS. Of these, 3566 (77.2%) met guideline-defined severe AS. Compared with the AI-derived low-probability AS group (19.2% mortality), the age-adjusted and sex-adjusted OR for actual 5-year mortality was 2.41 (95% CI 2.13 to 2.73) in the high probability AS group (67.9% mortality)-5-year mortality being slightly higher in those with guideline-defined severe AS (69.1% vs 64.4%; age-adjusted and sex-adjusted OR 1.26 (95% CI 1.04 to 1.53), p=0.021).
conclusionsAn AI-DSA can identify the echocardiographic measurement characteristics of AS associated with poor survival (with not all cases guideline defined). Deployment of this tool in routine clinical practice could improve expedited identification of severe AS cases and more timely referral for therapy.
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