ArticleNPJ cardiovascular health2024
Foundation models for cardiovascular disease detection via biosignals from digital stethoscopes.
Article in NPJ cardiovascular health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Foundation models for cardiovascular diseases and medicine.European heart journal. Digital health · 2026Review
- Wearable Flexible Sensors for Cardiovascular Disease Monitoring.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- End-to-End Platform for Electrocardiogram Analysis and Model Fine-Tuning: Development and Validation Study.Journal of medical Internet research · 2026Article
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- Bimodal ECG and PCG Cardiovascular Disease Detection: Exploring the Potential and Modality Contribution.Journal of medical systems · 2025Article
- Advancing global cardiovascular research and clinical translation.NPJ cardiovascular health · 2025Article
- Advances in cardiovascular signal analysis with future directions: a review of machine learning and deep learning models for cardiovascular disease classification based on ECG, PCG, and PPG signals.Biomedical engineering letters · 2025Review
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- Automated Detection of Reduced Ejection Fraction Using an ECG-Enabled Digital Stethoscope: A Large Cohort Validation.JACC. Advances · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Auscultation of the heart and the electrocardiogram (ECG) are two central components of the cardiac exam. Recent innovations of the stethoscope have enabled the simultaneous acquisition of a high-quality digital acoustic signal and ECG. We present foundation models trained on phonocardiogram (PCG) and ECG data collected from digital stethoscopes during routine clinical practice. We show that these foundation models that are pre-trained on large unlabeled datasets in a self-supervised manner can be fine-tuned for a variety of cardiovascular disease detection tasks. This is the first study that builds foundation models specifically for synchronously captured PCG and ECG data. Our approach is based on the recently developed masked autoencoder framework which we extend to handle multiple signals that are synchronously captured. This paradigm makes it possible to use large capacity models leading to superior performance even though the size of datasets with medical label annotations may be limited.
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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.