ArticleEuropean heart journal. Digital health2021
Artificial intelligence-assisted auscultation in detecting congenital heart disease.
Article in European heart journal. Digital health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.
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Who cites it
33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 54 citations in OpenAlex.
- Effectiveness of machine learning for diagnosis and prognostic prediction of congenital heart disease: a systematic review and meta-analysis.BMC pediatrics · 2026Pooled it
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- A review on deep learning methods for heart sound signal analysis.Frontiers in artificial intelligence · 2024Pooled it
- Pediatric murmur auscultation and tele-consultation: importance of signal quality and classification.Pediatric research · 2026Article
- Delayed Diagnosis of Congenital Heart Diseases in Adults: Opportunities for Improving Diagnostic Strategies.Journal of cardiovascular development and disease · 2026Review
- An Explainable Quality-Aware ECG-PCG Fusion for Cardiovascular Disease Detection Using Robust Feature Modeling.Diagnostics (Basel, Switzerland) · 2026Article
- Rational and design of the digital diagnosis of cardiac sounds in paediatric patients (DI_SOUND) study.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Pediatric Cardiology: Present Applications and Future Directions.Pediatric reports · 2026Review
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Hybrid vision transformer framework for congenital heart disease diagnosis.Scientific reports · 2026Article
- Advancing Point-of-Care Still's Murmur Identification: Evaluating the Efficacy of ConvNets and Transformers Using the StethAid Multicenter Heart Sound Database.IEEE transactions on bio-medical engineering · 2026Article
- Diagnostic performance of wearable acoustic-electrocardiographic indices for screening secundum atrial septal defect in children.Frontiers in pediatrics · 2026Article
- Prediction of significant congenital heart disease in infants and children using continuous wavelet transform and deep convolutional neural network with 12-lead electrocardiogram.BMC pediatrics · 2025Article
- Automated analysis of heart sound signals in screening for structural heart disease in children.European journal of pediatrics · 2024Article
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- The Evolving Stethoscope: Insights Derived from Studying Phonocardiography in Trainees.Sensors (Basel, Switzerland) · 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 Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.IEEE access : practical innovations, open solutions · 2024Article
- Accurately assessing congenital heart disease using artificial intelligence.PeerJ. Computer science · 2024Article
- The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
Corrections and comments
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Authors and funding
15 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: Computer-assisted auscultation has become available to assist clinicians with physical examinations to detect congenital heart disease (CHD). However, its accuracy and effectiveness remain to be evaluated. This study seeks to evaluate the accuracy of auscultations of abnormal heart sounds of an artificial intelligence-assisted auscultation (AI-AA) platform we create. Methods and results: Initially, 1397 patients with CHD were enrolled in the study. The samples of their heart sounds were recorded and uploaded to the platform using a digital stethoscope. By the platform, both remote auscultation by a team of experienced cardiologists from Shanghai Children's Medical Center and automatic auscultation of the heart sound samples were conducted. Samples of 35 patients were deemed unsuitable for the analysis; therefore, the remaining samples from 1362 patients (mean age-2.4 ± 3.1 years and 46% female) were analysed. Sensitivity, specificity, and accuracy were calculated for remote auscultation compared to experts' face-to-face auscultation and for artificial intelligence automatic auscultation compared to experts' face-to-face auscultation. Kappa coefficients were measured. Compared to face-to-face auscultation, remote auscultation detected abnormal heart sound with 98% sensitivity, 91% specificity, 97% accuracy, and kappa coefficient 0.87. AI-AA demonstrated 97% sensitivity, 89% specificity, 96% accuracy, and kappa coefficient 0.84. Conclusions: The remote auscultations and automatic auscultations, using the AI-AA platform, reported high auscultation accuracy in detecting abnormal heart sound and showed excellent concordance to experts' face-to-face auscultation. Hence, the platform may provide a feasible way to screen and detect CHD.
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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.