Trial reportPediatric cardiology2019
Artificial Intelligence-Assisted Auscultation of Heart Murmurs: Validation by Virtual Clinical Trial.
Trial report in Pediatric cardiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07542509 (Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]), which is not on this map. Cited by 52 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Digital diagnoSis of Cardiac sOUNd in peDiatric Patients [DI-SOUND Study]
Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 85 citations in OpenAlex.
- Congenital heart disease diagnosis using machine learning: a systematic literature review.Frontiers in medicine · 2026Pooled it
- Rational and design of the digital diagnosis of cardiac sounds in paediatric patients (DI_SOUND) study.European heart journal. Digital health · 2026Article
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- 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
- Machine learning-based early detection of abnormal heart rate in critically ill patients: a real-world clinical dataset study with automated alert system integration.Frontiers in artificial intelligence · 2026Article
- Proposing the ValvUS approach: integrating bedside tests and ultrasonography for severe valvular heart disease diagnosis.Einstein (Sao Paulo, Brazil) · 2026Review
- Detection of atrial fibrillation via artificial intelligence-assisted auscultation.BMC cardiovascular disorders · 2025Article
- Automatic Diagnosis of Left Valvular Heart Disease Based on Artificial Intelligence Stethoscope.JACC. Advances · 2025Article
- Essential Concepts in Artificial Intelligence: A Guide for Pediatric Providers.Children (Basel, Switzerland) · 2025Review
- Role of artificial intelligence in congenital heart disease.World journal of clinical pediatrics · 2025Review
- An evaluation of a five-year program for newborn congenital heart disease screening in Jiangsu Province, 2019-2023.BMC public health · 2025Article
- Artificial Intelligence in Cardiac Surgery: Transforming Outcomes and Shaping the Future.Clinics and practice · 2025Review
- Automated analysis of heart sound signals in screening for structural heart disease in children.European journal of pediatrics · 2024Article
- Automated valvular heart disease detection using heart sound with a deep learning algorithm.International journal of cardiology. Heart & vasculature · 2024Article
- Article
- Deep Learning in Heart Sound Analysis: From Techniques to Clinical Applications.Health data science · 2024Review
- A Noise-Robust Heart Sound Segmentation Algorithm Based on Shannon Energy.IEEE access : practical innovations, open solutions · 2024Article
- The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
- Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.Journal of the American Heart Association · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has potential to improve the accuracy of screening for valvular and congenital heart disease by auscultation. However, despite recent advances in signal processing and classification algorithms focused on heart sounds, clinical acceptance of this technology has been limited, in part due to lack of objective performance data. We hypothesized that a heart murmur detection algorithm could be quantitatively and objectively evaluated by virtual clinical trial. All cases from the Johns Hopkins Cardiac Auscultatory Recording Database (CARD) with either a pathologic murmur, an innocent murmur or no murmur were selected. The test algorithm, developed independently of CARD, analyzed each recording using an automated batch processing protocol. 3180 heart sound recordings from 603 outpatient visits were selected from CARD. Algorithm estimation of heart rate was similar to gold standard. Sensitivity and specificity for detection of pathologic cases were 93% (CI 90-95%) and 81% (CI 75-85%), respectively, with accuracy 88% (CI 85-91%). Performance varied according to algorithm certainty measure, age of patient, heart rate, murmur intensity, location of recording on the chest and pathologic diagnosis. This is the first reported comprehensive and objective evaluation of an AI-based murmur detection algorithm to our knowledge. The test algorithm performed well in this virtual clinical trial. This strategy can be used to efficiently compare performance of other algorithms against the same dataset and improve understanding of the potential clinical usefulness of AI-assisted auscultation.
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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.