Arrhythmias

Contrastive Multi-modal Training with Electrocardiography and Natural Language Echocardiography Reports for Zero-shot Prediction of Structural Heart Disease

Machine learning models for predicting structural heart disease (SHD) from electrocardiography (ECG) traditionally required structured echocardiographic data. The potential of echocardiography (ECHO) natural language reports remains underused. We describe MERL-ECHO, a multimodal model using contrastive language-image pre-training (CLIP) that aligns ECG with ECHO natural language reports for zero-s...

Does explainable AI-ECG heart age differentiate pathological from physiological LV remodeling? A multi-cohort analysis including young elite athletes

Artificial intelligence applied to electrocardiography (AI-ECG) can derive a heart age or ECG-age, potentially reflecting waveform patterns that indicate cumulative myocardial stress. The heart age gap (HA-gap, Δage) is defined as the difference between a person’s ECG-age and chronological age. Former studies suggest a threshold of Δage > 8 yrs as a biomarker for accelerated biological age, associ...

Artificial Intelligence in Cardiac Amyloidosis: A Systematic Review and Meta-Analysis of Diagnostic Accuracy Across Imaging and Non-Imaging Modalities

Cardiac amyloidosis (CA) is an underdiagnosed infiltrative cardiomyopathy associated with poor outcomes if not detected early. Artificial intelligence...

ECG classification with convolutional neural networks demonstrates resilience to sex-imbalances in data

Many ECG-AI models have been developed to predict a wide range of cardiovascular outcomes. The underrepresentation of women in cardiovascular disease ...

Short-term Repeatability of Artificial Intelligence Estimated Electrocardiographic Age

Advancements in artificial intelligence have enabled estimation of cardiac age from raw ECG waveforms. ECG-age is a novel metric that provides insight...

Machine learning to classify left ventricular hypertrophy using ECG feature extraction by variational autoencoder

Traditional ECG criteria for left ventricular hypertrophy (LVH) have modest diagnostic yield. Develop and validate machine learning models for LVH dia...

ECG-Derived Synthetic Tissue Doppler Waveforms Differentiate Physiological Adaptations in Healthy Athletes from Pathological Patterns Associated with Mortality in Young Individuals

Sudden cardiac death (SCD) in young individuals—especially athletes—remains difficult to diagnose due to overlapping physiological and pathological el...

Enhancing the Reliability of Resting ECGs via Deep Learning–Driven Motion Artifact Detection

This study presents a novel two-stage framework to enhance the reliability of resting electrocardiogram (ECG) signals by addressing motion artifacts t...

A deep learning ECG model for localization of occlusion myocardial infarction

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria m...

Forecasting left ventricular systolic dysfunction in heart failure with artificial intelligence

Objective assessment of left ventricular function remains a key prognosticator that is used to guide therapeutic decisions for patients with heart fai...

BeatAI: BiomEtrics for Atrial Arrhythmia Tracking Using Artificial Intelligence

Postoperative atrial fibrillation (POAF) affects 20 to 50% of patients undergoing cardiac surgery and is associated with longer hospital stays and adv...

Privacy-Aware Federated nnU-Net for ECG Page Digitization

Deep neural networks can convert ECG page images into analyzable waveforms, yet centralized training often conflicts with cross-institutional privacy ...

Intelligent Decision Support System Facilitating Early Detection of Cardiovascular Disease

Cardiovascular disease (CVD) remains the primary cause of mortality worldwide, with higher fatality rates in India. Multi-modal diagnostics integratin...

Translating Deep Learning to Clinical Practice: External Validation and Clinical Benefit of an Electrocardiogram-Based Neural Network for Detecting Low Ejection Fraction

Low ejection fraction (EF), an indicator of impaired heart function, often goes undiagnosed and can lead to avoidable heart failure and arrhythmias. W...

A foundation transformer model with self-supervised learning for ECG-based assessment of cardiac and coronary function

The wide availability of labeled electrocardiogram (ECG) data has driven major advances in artificial intelligence (AI)-based detection of structural ...

Seeing the Aging Heart: Multimodal AI Quantifies Cardiac Biological Aging from Angiography, Echocardiography, and ECG

Cardiac biological aging results in vascular, structural, and electrical changes that account for age-related cardiovascular disease. Using techniques...

Premature Ventricular Contraction-Mediated Ventricular Fibrillation: Clinical characteristics, Application of Machine-Learning Algorithm and Outcomes of Catheter Ablation: Multicentric Case Series

Premature ventricular contractions (PVCs) are common in patients with and without structural heart disease. In a subset of patients, PVCs are associat...

TARGET-AI: a foundational approach for the targeted deployment of artificial intelligence electrocardiography in the electronic health record

Artificial intelligence (AI) applied to routine electrocardiograms (ECGs) offers promise for screening of structural heart disease (SHD), yet broad cl...

An Explainable Advanced Electrocardiography Score for Diastolic Dysfunction - Derivation, Validation and Prognostic Performance

Diastolic dysfunction is a precursor to heart failure with preserved ejection fraction (HFpEF), and early detection by electrocardiography (ECG) would...

Deep Learning Prediction of Left Atrial Structure and Function from 12-lead Electrocardiograms

Abnormal cardiac atrial structure and function (atrial cardiopathy)1 typically precedes atrial fibrillation (AF) and predicts other cardiovascular com...