ArticleEuropean heart journal. Digital health2021
The discerning ear: cardiac auscultation in the era of artificial intelligence and telemedicine.
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. An erratum has been issued. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 17 citations in OpenAlex.
- TriFusion-PCGNet: A Hybrid Multi-Branch Deep Learning Network for Energy-Aware Normal/Abnormal Heart Sound Classification.Life (Basel, Switzerland) · 2026Article
- mHealth Technologies for the Care of Children With Congenital Heart Disease: Scoping Review.JMIR mHealth and uHealth · 2026Article
- DTF-STCANet: A Dual Time-Frequency Swin Transformer and ConvNeXt Attention Network for Heart Sound Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Machine learning-based predictive model for atrial arrhythmia following transcatheter atrial septal defect closure.International journal of cardiology. Congenital heart disease · 2025Article
- Machines Are Learning Chest Auscultation. Will They Also Become Our Teachers?CHEST pulmonary · 2024Review
- The Evolving Stethoscope: Insights Derived from Studying Phonocardiography in Trainees.Sensors (Basel, Switzerland) · 2024Article
- Assessment of a Teaching Module for Cardiac Auscultation of Horses by Veterinary Students.Animals : an open access journal from MDPI · 2024Article
- The Lifelong Impact of Artificial Intelligence and Clinical Prediction Models on Patients With Tetralogy of Fallot.CJC pediatric and congenital heart disease · 2023Review
- The future is more than a digital stethoscope.European heart journal. Digital health · 2021Article
Corrections and comments
- Erratum issuedErratum.2022
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart murmur, a thoracic auscultatory finding of cardiovascular origin, is extremely common in childhood and can appear at any age from premature newborn to late adolescence. The objective of this review is to provide a modern examination and update of cardiac murmur auscultation in this new era of artificial intelligence (AI) and telemedicine. First, we provide a comprehensive review of the causes and differential diagnosis, clinical features, evaluation, and long-term management of paediatric heart murmurs. Next, we provide a brief history of computer-assisted auscultation and murmur analysis, along with insight into the engineering design of the digital stethoscope. We conclude with a discussion of the paradigm shifting impact of deep learning on murmur analysis, AI-assisted auscultation, and the implications of these technologies on telemedicine in paediatric cardiology. It is our hope that this article provides an updated perspective on the impact of AI on cardiac auscultation for the modern paediatric cardiologist.
Indexed as
Identifiers
What OpenQuestion holds
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.