Arrhythmias

Identification of Hypertrophic Cardiomyopathy on Electrocardiographic Images with Deep Learning

Hypertrophic cardiomyopathy (HCM) is frequently underdiagnosed. While deep learning (DL) models using raw electrocardiographic (ECG) voltage data can enhance detection, their use at the point-of-care is limited. Here we report the development and validation of a DL model that detects HCM from images of 12-lead ECGs across layouts. The model was developed using 124,553 ECGs from 66,987 individuals ...

Artificial intelligence-enhanced Electrocardiography Score for Perioperative Risk Assessment in Non-cardiac Surgery

The role of electrocardiography (ECG) has been limited in the preoperative risk evaluation in noncardiac surgery due to its low prognostic value. Recent advances in artificial intelligence (AI) have enabled the extraction of subtle features from ECG that can be used in risk prediction. This study aimed to evaluate the utility of an AI-enabled ECG (QCG-Critical score) in predicting 30-day postopera...

AI-Driven Pharmacovigilance and Molecular Profiling of Fluoroquinolone-Associated Cardiotoxicity in the UAE: A Geospatial and Machine Learning Analysis with Structural Modification Strategies (2018-2023)

Fluoroquinolones, while clinically indispensable, carry underappreciated cardiovascular risks, particularly QT prolongation and life-threatening arrhy...

Novel Deep Learning Framework for Simultaneous Assessment of Left Ventricular Mass and Longitudinal Strain: Clinical Feasibility and Validation in Patients with Hypertrophic Cardiomyopathy

This study aims to present the Segmentation-based Myocardial Advanced Refinement Tracking (SMART) system, a novel artificial intelligence (AI)-based f...

Machine Learning-based Mortality Prediction for Pediatric Fulminant Myocarditis Using Cytokine Profiles

Fulminant myocarditis (FM) is a rare but life-threatening pediatric condition that rapidly progresses to cardiogenic shock and fatal arrhythmias. Earl...

Kolmogorov-Arnold Network for Atherosclerotic Cardiovascular Disease Risk Prediction

Assessing the risk of future atherosclerotic cardiovascular disease (ASCVD) is crucial in clinical practice, yet it continues to pose significant chal...

Causal machine learning for assessing the effectiveness of off-label use of amiodarone in new-onset atrial fibrillation

Off-label drug use, i.e., uses of a drug that differ from what regulatory authorities have approved, is common, occurring overall in up to 36% of pres...

Speaking the Language of Inclusion: Examining English Languages Requirements in Cardiovascular Digital Health Trials

Cardiovascular medicine is rapidly evolving, as it integrates digital technologies intended to decentralize care from the clinic and/or hospital setti...

Detection of Atrial Fibrillation with a Hybrid Deep Learning Model and Time-Frequency Representations

Atrial fibrillation (AF), a common cardiac arrhythmia, can lead to severe complications, emphasizing the urgent need for effective detection methods. ...

Wearable-Echo-FM: An ECG-echo foundation model for single lead electrocardiography

Artificial intelligence (AI) models can now detect patterns of structural heart diseases (SHDs) from electrocardiograms (ECGs), though scaling them re...

The miniECG: Enabling interpretable detection of amplitude and intraventricular conduction ECG-abnormalities with a novel ECG device

The miniECG, a smartphone-sized, multi-lead device, offers a simple and fast alternative to the 12-lead ECG. We aimed to demonstrate the potential of ...

MultiECGNet: A novel deep learning-based multi-format ensemble method for image-based electrocardiographic diagnosis of atrial fibrillation

To evaluate the performance of an ensemble classifier, MultiECGNet, using multi-format electrocardiographic (ECG) images for the diagnosis of atrial f...

A Novel Noise-Resilient and Explainable Machine Learning Framework for Accurate and Robust ECG-Based Heart Disease Diagnosis

An electrocardiogram (ECG) is essential for diagnosing cardiac abnormalities. Automated heartbeat classification enables continuous heart monitoring a...

Multiethnic Validation of Artificial Intelligence-Enhanced Electrocardiographic Image Analysis in Detecting Cardiac Structural and Functional Abnormalities: A UK Biobank Study

Although artificial intelligence–enhanced electrocardiography (AI-ECG) has shown promise in detecting cardiac abnormalities, large-scale validation ag...

Deep learning predicts cardiac output from seismocardiographic signals in heart failure

Determination of cardiac output (CO) is essential to the clinical management of cardiovascular compromise. However, the invasiveness, procedural risks...

Personalized Synthetic Electrocardiograms with Outcomes

Synthetic data can be the solution to privacy requirements, can enrich datasets limited by underrepresentation of certain subgroups/minorities, combat...

HuBERT-ECG as a self-supervised foundation model for broad and scalable cardiac applications

Deep learning models have shown remarkable performance in electrocardiogram (ECG) analysis, but the limited availability and size of ECG datasets have...

Phenotypic Selectivity of Artificial Intelligence-enhanced Electrocardiography in Cardiovascular Diagnosis and Risk Prediction

Artificial intelligence (AI)-enhanced electrocardiogram (ECG) models are designed to detect specific anatomical and functional cardiac abnormalities. ...

Fusion-Based Deep Learning Ensemble on MIT-BIH and PTB-XL ECG Databases for Enhanced Cardiac Diagnosis

Electrocardiogram (ECG) analysis plays a critical role in the early detection and diagnosis of cardiac abnormalities. In this study, we propose a fusi...

Estimating ascending aortic diameter from the electrocardiogram

In an analysis of 69,173 UK Biobank participants, we paired MRI-based measurements of the ascending aortic diameter with ECG signal. We trained a 1D c...