Arrhythmias

Fast cardiac magnetic resonance (CMR) protocol for biventricular functional assessment and tissue characterisation.

BACKGROUND: Clinical use of cardiovascular magnetic resonance (CMR), reference tool for cardiac function and myocardial tissue assessment, is frequently limited by long acquisition times. This study aimed to compare conventional "standard protocol" (ASSET bSSFP cine plus 2D single-segment PSIR LGE) with novel "fast protocol" incorporating deep-learning reconstruction (Sonic DL bSSFP cine and 2D mu...

Biatrial cardioneuroablation guided by robotic magnetic navigation and artificial intelligence-based mapping in vagal bradyarrhythmias: A controlled observational study.

PURPOSE: Reflex bradyarrhythmias and syncope related to excessive vagal tone may be refractory to conservative therapy and significantly impair quality of life. Cardioneuroablation (CNA) has emerged as a device-sparing alternative, but real-world outcome data remain limited. METHODS: We retrospectively evaluated 12 consecutive patients (aged 23-55 years) with drug-refractory, vagally mediated brad...

[Wolff-Parkinson-White syndrome : Comparison of different algorithms].

BACKGROUND: Wolff-Parkinson-White (WPW) syndrome is characterised by accessory pathways that bypass the normal atrioventricular conduction system. Pre...

Foundation models for electrocardiogram interpretation: clinical implications.

BACKGROUND AND AIMS: The 12-lead electrocardiogram (ECG) remains a cornerstone of cardiac diagnostics, yet existing artificial intelligence (AI) solut...

Contemporary implantable cardiac device trials for sudden cardiac death and future guidelines: how can we shape the future?

INTRODUCTION: Sudden cardiac death (SCD) remains a major cause of mortality despite substantial progress in heart failure management and arrhythmia pr...

A 1.69µJ Highly Robust Cardiac Arrhythmia Monitoring Processor with Triple-Adaptive QRS Detector and Medically Driven Feature-Fusion Hybrid Neural Networks.

The detection of arrhythmias is crucial in monitoring cardiac health. However, electrocardiogram (ECG) signals obtained from wearable devices are ofte...

Domain Knowledge is Power: Leveraging Physiological Priors for Self-Supervised Representation Learning in Electrocardiography.

Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG ana...

Data-driven pediatric ECG reference intervals with VSD-based validation.

To establish population-specific, age- and sex-stratified electrocardiographic (ECG) reference ranges for Chinese children and adolescents using a dat...

A hybrid hierarchical transformer model for ECG classification and age prediction.

Electrocardiograms (ECGs) play a crucial role in cardiovascular healthcare, requiring effective analytical models. ECG analysis is inherently hierarch...

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

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

[Enhancement of radiomics-based machine learning models for predicting efficacy of high-intensity focused ultrasound ablation of uterine fibroids using undersampling methods].

OBJECTIVES: To improve the accuracy of machine learning models for preoperative prediction of high-intensity focused ultrasound (HIFU) ablation effica...

[Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion].

OBJECTIVES: To enhance the accuracy and reliability of 12-lead electrocardiogram (ECG) automatic diagnosis. METHODS: Herein we propose a 12-lead ECG a...

Mapping Algorithmic Bias in AI-Powered Electrocardiogram Interpretation Across the AI Life Cycle: Protocol for a Scoping Review.

BACKGROUND: Artificial intelligence (AI)-powered analysis of electrocardiograms (ECGs) is reshaping cardiac diagnostics, offering faster and often mor...

uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm.

INTRODUCTION: Premature Ventricular Contractions (PVCs) are common cardiac arrhythmias originating from the ventricles. Accurate detection remains cha...

Integrative multimodal hybrid data fusion for mortality prediction.

Multimodal Machine Learning (MML) methods address various efficient ways of driving insights from various data modalities, e.g., in healthcare setting...

Head-to-head comparison of the ability of the cardiometabolic index and triglyceride-glucose index to predict 3-year major adverse cardiovascular events in patients with atrial fibrillation: insights from a community cohort.

BACKGROUND: Atrial fibrillation (AF) represents the most common sustained cardiac arrhythmia and confers an elevated risk of major adverse cardiovascu...

FetCAT: Cross-attention fusion of transformer-CNN architecture for fetal brain plane classification with explainability using motion-degraded MRI.

Fetal brain magnetic resonance imaging (MRI) has been recognized as a vital diagnostic tool for identifying neurological anomalies during pregnancy. A...

Analytical Approaches for the Determination of Lidocaine and Its Metabolites: A Comprehensive Insight.

Lidocaine (LID), an amide-type local anesthetic and antiarrhythmic agent, remains one of the most extensively used drugs in medical, dental, and surgi...

Prediction of left ventricular systolic dysfunction in left bundle branch block using a fine-tuned ECG foundation model.

Left bundle branch block (LBBB) is an important electrocardiographic (ECG) finding strongly associated with left ventricular systolic dysfunction (LVS...

Wearable device derived electrocardiographic age and its association with atrial fibrillation.

Artificial intelligence (AI)-derived electrocardiographic (ECG) age is a promising marker of atrial fibrillation (AF) risk. We developed PROPHECG-Age ...