Arrhythmias

Transfer learning for Multi-institutional Classification of Intussusception and Splenomegaly in Pediatric Abdominal Radiographs.

To address diagnostic delays in pediatric abdominal emergencies, this study aimed to develop and validate multi-institutional deep learning models for detecting intussusception and splenomegaly on abdominal radiographs, thereby evaluating their potential to enhance clinical triage. This retrospective study included 26,552 radiographs from seven tertiary hospitals (2012-2022). Models were trained w...

Machine learning-based prediction of diabetic retinopathy using clinlabomics: a multi-center study.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of vision loss, yet conventional retinal screening remains costly and resource-intensive. This study developed and validated machine-learning (ML) models using routine laboratory data to provide a cost-effective, accessible alternative for DR risk stratification and triage. METHODS: We analyzed data from 750 patients (363 T2DM; 387 DR) and e...

Artificial Intelligence Versus Electrophysiologist Adjudication of Atrial Arrhythmias.

EJHF expert consensus statement on the diagnosis and management of hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is the most prevalent genetic cardiac disease and a leading cause of heart failure, arrhythmia, and sudden cardiac d...

Stacking Deep Neural Networks to Detect Multiple Types of Cardiac Arrhythmias.

Heart arrhythmias are associated with serious cardiovascular diseases and can result in fatal outcomes if not diagnosed early. Electrocardiograms (ECG...

Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a public health burden with the majority occurring in the general population for whom there is no...

Minority class-aware multiclass arrhythmia detection using conditional GAN augmentation.

The detection of cardiac arrhythmias from electrocardiogram (ECG) signals is essential for preventing sudden cardiac deaths. However, model performanc...

Atrial repolarization wave: a new approach using spline-based feature engineering and explainable AI for atrial arrhythmia diagnosis.

OBJECTIVES: The atrial repolarization (Ta wave) characteristics remains largely unexplored, given its inherently low amplitude and obscured by the QRS...

Multi-modal federated learning with differential privacy for privacy-preserving healthcare AI.

The growing adoption of artificial intelligence in healthcare highlights the need for models that can leverage heterogeneous patient data while preser...

Clinician engagement shapes the impact of AI-based ECG screening for chronic liver disease in primary care.

Artificial intelligence (AI)-based screening tools show promise for early identification of chronic liver disease (CLD), yet their effectiveness in re...

Predicting atrial fibrillation recurrence after radiofrequency ablation using the AIFV system: a prospective observational study.

BACKGROUND: Pulmonary vein isolation (PVI) has become the cornerstone of atrial fibrillation (AF) treatment. Nevertheless, the efficacy of radiofreque...

Novel machine learning fusion architectures integrating electrocardiogram representations: applications to acute coronary event detection.

AIMS: AI in electrocardiography (ECG) has diverged into two paths: traditional signal processing with machine learning, and deep learning of raw wavef...

Incremental Prognostic Power of Coronary Flow Reserve and Artificial Intelligence-Enabled ECG-Derived Filling Pressure in Angina With Nonobstructive Coronary Arteries.

BACKGROUND: Coronary flow reserve reflects microvascular function, whereas filling pressure indicates myocardial hemodynamic burden. In angina with no...

Early volume reduction slope as a novel predictor of long-term outcomes following microwave ablation of benign thyroid nodules: a machine learning-enhanced prediction model.

OBJECTIVE: This study aimed to develop and validate a predictive model incorporating early VRR slope kinetics to predict long-term treatment outcomes....

Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya.

IMPORTANCE: Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited ac...

From Fitness to Cognition: Machine-Learning Prediction of Cognitive Performance Using Physiological Parameters in Healthy Adults.

INTRODUCTION: Current cognitive tasks are not suitable for frequent monitoring of cognitive function in healthy adults. Increasing evidence suggests t...

Machine learning-based information flow analysis of ECG signals for long QT syndrome.

Cardiovascular diseases remain the leading cause of death worldwide, highlighting the need for non-invasive and cost-effective risk assessment tools. ...

Evaluating AI-Enabled Technologies for Atrial Fibrillation Detection: A Systematic Review of Diagnostic Performance.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide and a major contributor to stroke and cardiovascular morbidity....

Getting ahead of sudden cardiac arrest in pediatric hypertrophic cardiomyopathy patients.

INTRODUCTION: Sudden cardiac arrest (SCA) remains one of the most devastating complications of pediatric hypertrophic cardiomyopathy (HCM). Despite ma...

A large-scale 12-lead electrocardiogram dataset for acute coronary syndrome prediction containing 19,955 ECGs.

Acute coronary syndrome(ACS) is a common cardiovascular disease and a severe type of coronary heart disease. Electrocardiograms(ECGs) are the initial ...