ArticleJournal of the American Society of Echocardiography : official publication of the American Society of Echocardiography2023
Automated Detection of Aortic Stenosis Using Machine Learning.
Article in Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 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
- 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
- Standardizing Vegetation Size Measurement in Native Left-Sided Infective Endocarditis Using Artificial Intelligence.Journal of clinical medicine · 2026Article
- Accuracy of a Deep Learning Model in Intracardiac Echocardiography.JACC. Advances · 2026Article
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging.Current cardiology reports · 2026Review
- Value of an automated machine learning model with post-hoc explanation for predicting healthcare-seeking delays among residents in Tibetan regions.Frontiers in public health · 2026Article
- Assessment of HeartModel® automated left ventricular ejection fraction for patients with hypertrophic cardiomyopathy.European heart journal. Imaging methods and practice · 2026Article
- EchoFM: Foundation Model for Generalizable Echocardiogram Analysis.IEEE transactions on medical imaging · 2025Article
- Aortic valve leaflet motion for diagnosis and classification of aortic stenosis using single view echocardiography.Journal of cardiovascular imaging · 2025Article
- Contemporary applications of artificial intelligence and machine learning in echocardiography.NPJ cardiovascular health · 2025Review
- New Threshold for Defining Mild Aortic Stenosis Derived From Velocity-Encoded MRI in 60,000 Individuals.Journal of the American College of Cardiology · 2025Article
- Echocardiographic characteristics of paravalvular leak following surgical aortic valve replacement: a retrospective cohort study.Journal of thoracic disease · 2025Article
- Discordance between aortic valve gradient and area: do I trust the significant gradient?European heart journal supplements : journal of the European Society of Cardiology · 2025Article
- Machine learning-enabled screening for aortic stenosis with handheld ultrasound.European heart journal. Imaging methods and practice · 2025Article
- Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review.Frontiers in cardiovascular medicine · 2025Review
- A Multimodality Video-Based AI Biomarker For Aortic Stenosis Development And Progression.medRxiv : the preprint server for health sciences · 2024Article
- Detecting Heart Disease from Multi-View Ultrasound Images via Supervised Attention Multiple Instance Learning.Proceedings of machine learning research · 2023Article
- Echocardiographic Evaluation of Aortic Stenosis: A Comprehensive Review.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
14 authors.
Funding
Abstract
backgroundAortic stenosis (AS) is a degenerative valve condition that is underdiagnosed and undertreated. Detection of AS using limited two-dimensional echocardiography could enable screening and improve appropriate referral and treatment of this condition. The aim of this study was to develop methods for automated detection of AS from limited imaging data sets.
methodsConvolutional neural networks were trained, validated, and tested using limited two-dimensional transthoracic echocardiographic data sets. Networks were developed to accomplish two sequential tasks: (1) view identification and (2) study-level grade of AS. Balanced accuracy and area under the receiver operator curve (AUROC) were the performance metrics used.
resultsAnnotated images from 577 patients were included. Neural networks were trained on data from 338 patients (average n = 10,253 labeled images), validated on 119 patients (average n = 3,505 labeled images), and performance was assessed on a test set of 120 patients (average n = 3,511 labeled images). Fully automated screening for AS was achieved with an AUROC of 0.96. Networks can distinguish no significant (no, mild, mild to moderate) AS from significant (moderate or severe) AS with an AUROC of 0.86 and between early (mild or mild to moderate AS) and significant (moderate or severe) AS with an AUROC of 0.75. External validation of these networks in a cohort of 8,502 outpatient transthoracic echocardiograms showed that screening for AS can be achieved using parasternal long-axis imaging only with an AUROC of 0.91.
conclusionFully automated detection of AS using limited two-dimensional data sets is achievable using modern neural networks. These methods lay the groundwork for a novel method for screening for AS.
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