Myocardial Infarction

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 and deployment constraints. A cross-silo federated digitization framework is presented that trains a full-model nnU-Net segmentation backbone without sharing images and aggregates updates across sites under realistic non-IID heterogeneity (layout, gr...

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 integrating electrocardiogram (ECG) analysis, cardiac biomarkers, and region-specific insights can enhance early detection and clinical triage. In this cross-sectional study, ECGs along with clinico-epidemiological data were collected from two regions-North an...

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...

Deep learning enables fully automated cineCT-based assessment of regional right ventricular function

Right ventricular (RV) function is a key factor in the diagnosis and prognosis of heart disease. However, current advanced CT-based assessments rely o...

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...

Artificial Intelligence-Enabled Electrocardiogram for Elevated Left Ventricular Filling Pressure

Left ventricular filling pressure (LVFP) is associated with heart failure symptoms, a key prognostic marker, and a therapeutic target, but is difficul...

Cardiac Classification with Multi-Scale Convolutional Neural Network From Paper ECG

In cardiology, the classification of electrocardiograms (ECGs) or heartbeats serves as a vital instrument. Techniques grounded in deep learning for EC...

Deep learning on 3D ECG geometry predicts ischemia

Three-dimensional (3D) electrocardiography (ECG) is a recent methodological advance that extends the dimensionality of the standard ECG, enabling geom...

Prediction of Pulmonary Vein Isolation and Gap Recurrence on 12-Lead ECG Using Deep Learning

Pulmonary vein isolation (PVI) is key to atrial fibrillation (AF) ablation, but arrhythmia often recurs due to conduction gaps permitting pulmonary ve...

A Fast, Lightweight, and Generalizable Deep Neural Network for the Detection of Atrial Fibrillation

Atrial fibrillation (AFib) represents a critical diagnostic challenge in clinical cardiology, calling for automated detection systems capable of robus...

Artificial Intelligence for Significant Mitral Regurgitation Screening and Diagnosis: A Systematic Review and Meta-analysis

To evaluate performance of artificial intelligence (AI) models using electrocardiogram (ECG) and echocardiogram (ECHO) for predicting significant mitr...

Medication-Stratified Analysis of LDL-C Equation Miscalibration in Diabetes: Evidence from the All of Us Research Program and a Medication-Agnostic Machine-Learning Correction

Standard LDL-C equations were derived in cohorts largely untreated with modern combination diabetes therapies. With medication-treated patients compri...

Comparing different types of machine learning models in diagnosing diabetes mellitus utilizing electrocardiography and clinical data

Diabetes Mellitus (DM) represents one of the most significant global public health challenges of the 21st century. This dramatic increase in the preva...

The Silent Signal: Unmasking Myocardial Ischemia in a Resting Heartbeat with Machine Learning

Ischemic heart disease (IHD) remains the leading cause of morbidity and mortality worldwide, imposing a staggering burden on healthcare systems and so...

CardioForest: An Explainable Ensemble Learning Model for Automatic Wide QRS Complex Tachycardia Diagnosis from Ecg *

This study aims to develop and evaluate an ensemble machine learning-based framework for the automatic detection of Wide QRS Complex Tachycardia (WCT)...

Reliability of Artificial Intelligence-enhanced Electrocardiography

The scientific literature on artificial intelligence-enabled electrocardiography (AI-ECG) has defined a robust performance of AI models in detecting a...