ArticleJournal of the American Heart Association2024
Using Artificial Intelligence for Rheumatic Heart Disease Detection by Echocardiography: Focus on Mitral Regurgitation.
Article in Journal of the American Heart Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 35 citations in OpenAlex.
- Early Prediction of Cardiomyopathy in Pediatric Cancer Survivors Using Echocardiographic Video-Based Deep Learning.Echocardiography (Mount Kisco, N.Y.) · 2026Article
- Operational performance of the 2023 World Heart Federation screening criteria for rheumatic heart disease in a high-risk, resource-limited population.Open heart · 2026Article
- Multimodal Fusion of Echocardiogram Images and Electronic Medical Records for Heart Disease Screening: Retrospective Algorithm Development and Validation Study.JMIR medical informatics · 2026Article
- Artificial intelligence in echocardiography: Applications and future directions.Fundamental research · 2026Review
- A perspective on AI implementation in medical imaging in LMICs: challenges, priorities, and strategies.European radiology · 2026Article
- 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
- From innovation to implementation: Addressing the AI adoption gap in cardiac surgery.American heart journal plus : cardiology research and practice · 2026Review
- Delivering Rheumatic Heart Disease Diagnosis at Scale: Performance of an Integrated Public Health Screening Program in Northern Uganda.Journal of the American Heart Association · 2026Article
- Deep Learning-Based Automated Echocardiographic Measurements in Pediatric and Congenital Heart Disease.medRxiv : the preprint server for health sciences · 2026Article
- The Emerging Role of Artificial Intelligence in the Assessment of Valvular Heart Disease with Cardiac Imaging.Current cardiology reports · 2026Review
- Evaluation of a training program for rheumatic heart disease screening integrated into the public health system in Uganda.PloS one · 2026Article
- Update regarding the diagnosis and management of rheumatic heart disease in 2026.Sudanese journal of paediatrics · 2026Article
- Artificial intelligence in cardiology: implications for healthcare outcomes.Frontiers in artificial intelligence · 2026Review
- Machine learning for classification of advanced rheumatic heart disease using electrocardiogram in cardiology ward.BMC cardiovascular disorders · 2025Article
- Review
- A predictive model for the treatment outcomes of patients with secondary mitral regurgitation based on machine learning and model interpretation.BMC medical informatics and decision making · 2025Article
- A Narrative Review on Endomyocardial Fibrosis: Unraveling an Under-Recognized Tropical Heart Disease.Cureus · 2025Review
- From Echocardiography to CT/MRI: Lessons for AI Implementation in Cardiovascular Imaging in LMICs-A Systematic Review and Narrative Synthesis.Bioengineering (Basel, Switzerland) · 2025Review
- A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM.Scientific reports · 2025Article
- Contemporary applications of artificial intelligence and machine learning in echocardiography.NPJ cardiovascular health · 2025Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundIdentification of children with latent rheumatic heart disease (RHD) by echocardiography, before onset of symptoms, provides an opportunity to initiate secondary prophylaxis and prevent disease progression. There have been limited artificial intelligence studies published assessing the potential of machine learning to detect and analyze mitral regurgitation or to detect the presence of RHD on standard portable echocardiograms. METHODS AND
resultsWe used 511 echocardiograms in children, focusing on color Doppler images of the mitral valve. Echocardiograms were independently reviewed by an expert adjudication panel. Among 511 cases, 229 were normal, and 282 had RHD. Our automated method included harmonization of echocardiograms to localize the left atrium during systole using convolutional neural networks and RHD detection using mitral regurgitation jet analysis and deep learning models with an attention mechanism. We identified the correct view with an average accuracy of 0.99 and the correct systolic frame with an average accuracy of 0.94 (apical) and 0.93 (parasternal long axis). It localized the left atrium with an average Dice coefficient of 0.88 (apical) and 0.9 (parasternal long axis). Maximum mitral regurgitation jet measurements were similar to expert manual measurements (
conclusionsArtificial intelligence has the potential to detect RHD as accurately as expert cardiologists and to improve with more data. These innovative approaches hold promise to scale echocardiography screening for RHD.
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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.