ReviewPLOS digital health2023
Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022.
Review in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Enhancing pediatric cardiac auscultation with data-driven murmur detection: toward tele-consultation applications.Pediatric research · 2026Article
- Advancing cardiovascular screening: deep learning-based heart-sound classification using SMOTE and temporal modeling.Scientific reports · 2026Article
- Heart Murmur Detection in Phonocardiogram Data Leveraging Data Augmentation and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2025Article
- Attention-Fusion-Based Two-Stream Vision Transformer for Heart Sound Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Multiscale analysis of heart sound signals in the wavelet domain for heart murmur detection.Scientific reports · 2025Article
- AI-Enhanced Detection of Heart Murmurs: Advancing Non-Invasive Cardiovascular Diagnostics.Sensors (Basel, Switzerland) · 2025Article
- Enhancement of phonocardiogram segmentation using convolutional neural networks with Fourier transform module.Biomedical engineering letters · 2025Review
- Prediction of Left Ventricle Pressure Indices Via a Machine Learning Approach Combining ECG, Pulse Oximetry, and Cardiac Sounds: a Preclinical Feasibility Study.Journal of cardiovascular translational research · 2024Article
- From theoretical models to practical deployment: A perspective and case study of opportunities and challenges in AI-driven cardiac auscultation research for low-income settings.PLOS digital health · 2024Article
- A recurrent neural network and parallel hidden Markov model algorithm to segment and detect heart murmurs in phonocardiograms.PLOS digital health · 2024Article
- Phonocardiogram (PCG) Murmur Detection Based on the Mean Teacher Method.Sensors (Basel, Switzerland) · 2024Article
- Development of a Tremor Detection Algorithm for Use in an Academic Movement Disorders Center.Sensors (Basel, Switzerland) · 2024Article
- Murmur identification and outcome prediction in phonocardiograms using deep features based on Stockwell transform.Scientific reports · 2024Article
- Multiple instance learning framework can facilitate explainability in murmur detection.PLOS digital health · 2024Article
- Deep Learning Algorithms to Detect Murmurs Associated With Structural Heart Disease.Journal of the American Heart Association · 2023Article
- MCHeart: Multi-Channel-Based Heart Signal Processing Scheme for Heart Noise Detection Using Deep Learning.Biology · 2023Article
- Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022.PLOS digital health · 2023Review
- Beyond Heart Murmur Detection: Automatic Murmur Grading From Phonocardiogram.IEEE journal of biomedical and health informatics · 2023Article
- An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine Learning.Bioengineering (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs, who may need follow-up diagnostic screening and treatment for abnormal cardiac function. However, experts are needed to interpret the heart sounds, limiting the accessibility of cardiac auscultation in resource-constrained environments. Therefore, the George B. Moody PhysioNet Challenge 2022 invited teams to develop algorithmic approaches for detecting heart murmurs and abnormal cardiac function from phonocardiogram (PCG) recordings of heart sounds. For the Challenge, we sourced 5272 PCG recordings from 1452 primarily pediatric patients in rural Brazil, and we invited teams to implement diagnostic screening algorithms for detecting heart murmurs and abnormal cardiac function from the recordings. We required the participants to submit the complete training and inference code for their algorithms, improving the transparency, reproducibility, and utility of their work. We also devised an evaluation metric that considered the costs of screening, diagnosis, misdiagnosis, and treatment, allowing us to investigate the benefits of algorithmic diagnostic screening and facilitate the development of more clinically relevant algorithms. We received 779 algorithms from 87 teams during the Challenge, resulting in 53 working codebases for detecting heart murmurs and abnormal cardiac function from PCG recordings. These algorithms represent a diversity of approaches from both academia and industry, including methods that use more traditional machine learning techniques with engineered clinical and statistical features as well as methods that rely primarily on deep learning models to discover informative features. The use of heart sound recordings for identifying heart murmurs and abnormal cardiac function allowed us to explore the potential of algorithmic approaches for providing more accessible diagnostic screening in resource-constrained environments. The submission of working, open-source algorithms and the use of novel evaluation metrics supported the reproducibility, generalizability, and clinical relevance of the research from the Challenge.
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.