ArticlePNAS nexus2023
Deep learning unmasks the ECG signature of Brugada syndrome.
Article in PNAS nexus, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- Artificial Intelligence in Sports Cardiology: Advancing Cardiovascular Screening and Diagnosis.Cureus · 2026Review
- Synthetic electrocardiograms for Brugada syndrome: from data generation to expert cardiologists evaluation.European heart journal. Digital health · 2025Article
- Hearts, Data, and Artificial Intelligence Wizardry: From Imitation to Innovation in Cardiovascular Care.Biomedicines · 2025Review
- Self-supervised VICReg pre-training for Brugada ECG detection.Scientific reports · 2025Article
- A Vision Transformer Model for the Prediction of Fatal Arrhythmic Events in Patients with Brugada Syndrome.Sensors (Basel, Switzerland) · 2025Article
- Artificial intelligence for Brugada syndrome diagnosis and gene variants interpretation.American journal of cardiovascular disease · 2025Review
- Artificial Intelligence in Sports Medicine: Reshaping Electrocardiogram Analysis for Athlete Safety-A Narrative Review.Sports (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One in 10 cases of sudden cardiac death strikes without warning as the result of an inherited arrhythmic cardiomyopathy, such as Brugada Syndrome (BrS). Normal physiological variations often obscure visible signs of this and related life-threatening channelopathies in conventional electrocardiograms (ECGs). Sodium channel blockers can reveal previously hidden diagnostic ECG features, however, their use carries the risk of life-threatening proarrhythmic side effects. The absence of a nonintrusive test places a grossly underestimated fraction of the population at risk of SCD. Here, we present a machine-learning algorithm that extracts, aligns, and classifies ECG waveforms for the presence of BrS. This protocol, which succeeds without the use of a sodium channel blocker (88.4% accuracy, 0.934 AUC in validation), can aid clinicians in identifying the presence of this potentially life-threatening heart disease.
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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.