Arrhythmias

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 difficult to measure non-invasively. We aimed to develop and validate a deep learning-based artificial intelligence (AI) model using a standard 12-lead electrocardiogram (ECG) to detect elevated LVFP and assess its prognostic value. We trained an AI model to...

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 ECG signal examination support medical professionals in swiftly identifying heart ailments, thereby aiding in life preservation. The present investigation endeavors to convert a dataset comprising ECG record images into time-series signals, followed by...

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...

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...

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...

A Comparative Analysis of Supervised and Unsupervised Learning Methods for Normal-Abnormal Heartbeat Classification

In this study, the performances of 33 supervised and unsupervised machine learning methods for the automatic classification of cardiac arrhythmias as ...

Deep Learning Driven Field Dose Prediction for Head and Neck Cancer Treated with Spot Scanning Proton Therapy

Accurate dose prediction is essential for automating radiotherapy planning. In spot scanning proton therapy (SSPT), dose evaluation is required at bot...

Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison Study

Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfunction (LVSD...

Long-term, ambulatory 12-lead ECG from a single non-standard lead using perceptual reconstruction

Despite its broadening indications, the implantable cardiac monitor (ICM) records a narrow, nonstandard electrocardiogram (ECG) signal which precludes...

Artificial intelligence-driven ECG biomarkers for screening of large pericardial effusion

Pericardial effusion can progress to life-threatening cardiac tamponade when large or rapidly accumulating, yet early diagnosis is frequently delayed ...

An Artificial Intelligence Model for Detection of Heart Failure with Preserved Ejection Fraction: A Report from HeartShare Study

Heart failure with preserved ejection fraction (HFpEF) accounts for over half of all heart failure cases in the United States and remains a diagnostic...

Multitask Artificial Intelligence–Based Electrocardiogram Tool for Preoperative Cardiac Testing in Noncardiac Surgery: Retrospective Cohort Study of Health Care Utilization and Costs

Preoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing ...

Early Detection of Cardiovascular Disease Risk Using Multi-Parameter Biomarker Analysis and Machine Learning: A Prospective Cohort Study

Cardiovascular disease (CVD) remains the leading cause of mortality globally, with many events occurring in individuals without prior diagnosed condit...

Deep learning enhanced magnetocardiography enables multi-task detection of coronary, ventricular, and rhythm disorders

Magnetocardiography (MCG) captures the magnetic fields generated by myocardial currents, theoretically preserving electrophysiological details lost in...

Automatic quantification of left atrium volume for cardiac rhythm analysis leveraging 3D residual UNet for time-varying segmentation of ECG-gated CT.

Atrial fibrillation (AF) is a heart condition widely recognized as a significant risk factor for stroke. Left atrial (LA) volume variation has been id...

Can Intra-Operative Ablation-Specific Features Based on Ultrasound Fusion Imaging be Used to Predict Early Recurrence of Hepatocellular Carcinoma After Microwave Ablation: A Proof-of-Concept Study.

PURPOSE: Intra-operative factors are crucial to early recurrence of hepatocellular carcinoma (HCC) after microwave ablation (MWA), but few models have...