Arrhythmias

A Dual Soft Attention Network for Automatic Sleep Staging via Single-Lead Electrocardiogram Signals.

Polysomnography (PSG) is the current gold standard for sleep monitoring; however, it is complex, bur...

Readability and Quality of Online Patient Education Materials: Thyroid Radiofrequency Ablation.

OBJECTIVE: To assess the readability and quality of online patient education materials regarding thy...

Multimodal discovery of a pathogenic B cell-dependent T cell state in multiple sclerosis.

B cell depleting therapies in multiple sclerosis (MS) have transformed disease management, yet the i...

Beyond pooled analysis: sex-specific cardiovascular adverse event reporting signals with antiarrhythmic drugs in FAERS.

Sex differences in drug metabolism and cardiac electrophysiology may influence the cardiovascular sa...

A deep learning framework for arrhythmia classification: mitigating data leakage and class imbalance in ECG analysis.

Accurate and timely identification of cardiac abnormalities remains essential for reducing mortality...

Effects of image reconstruction methods and metal artefact reduction on the analysability and quantification of coronary artery calcium near a pacemaker lead: A phantom study.

INTRODUCTION: Coronary artery calcium (CAC) scoring using non-contrast cardiac CT is widely used for...

Artificial intelligence-based neonatal heart rate monitoring technologies: Systematic review.

BACKGROUND: Neonatal heart rate (HR) is an important parameter in the evaluation of newborn health a...

CT-based radiomics and ablation margin quantification for predicting local tumor progression in Stage I NSCLC after radiofrequency ablation.

Local tumor progression (LTP) compromises durable local control after radiofrequency ablation (RFA) ...

AI-driven ablative margin assessment after HCC radiofrequency ablation: a case-control feasibility study.

OBJECTIVE: Thermal ablation of hepatocellular carcinoma (HCC) requires an adequate safety margin aro...

Development and validation of machine learning models to predict 30-day mortality in patients with cardiac arrest complicated by acute kidney injury.

BACKGROUND: Cardiac Arrest (CA) often leads to Acute Kidney Injury (AKI), presenting a major health ...

Multimodal DCE-MRI stacking fusion for luminal versus non-luminal classification in breast cancer: A multicenter study with interpretability and ablation analysis.

PURPOSE: To develop and externally validate a multimodal stacking fusion model based on dynamic cont...

Machine Learning for Autism Spectrum Disorder Prediction: A Review of Data Augmentation and Feature Selection Techniques.

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent...

Post AF Ablation Anticoagulation: Real World Practice and Predictors of Therapy.

BACKGROUND: Oral anticoagulation (OAC) is recommended for ≥3 months post catheter ablation (CA) for ...

Memristor-based echo state network for ECG classification: optimizing accuracy and efficiency with different architectures.

Electrocardiogram (ECG) signal classification is a crucial task in automated cardiac monitoring and ...

PVCuRe: a machine-learning based tool to predict left ventricular systolic function recovery in patients undergoing ablation for premature ventricular contraction.

BACKGROUND: Ablation of frequent premature ventricular complexes (PVCs) can improve left ventricular...

Combined Unipolar and Bipolar Voltage Mapping at Redo Ablation After Cryoballoon Pulmonary Vein Isolation: Associations With Subsequent Arrhythmia Recurrence.

Harmonizing complexity and efficiency: Helix Fusion HarmonyNet's breakthrough in EEG-based postoperative delirium recognition.

Postoperative delirium (POD) is a common perioperative complication involving central nervous system...

Cost-Effectiveness of AI-Enabled Electrocardiography for Screening MASLD-Associated Advanced Chronic Liver Disease in the US Population.

BACKGROUND: MASLD is the most prevalent chronic liver disease worldwide, with pooled global prevalen...

Predicting Histotripsy Focal Shifts in the Liver From Acoustic Aberrations Using a Deep Learning Model.

OBJECTIVE: To develop a deep learning model for predicting histotripsy focal shifts in the liver cau...

Development and external validation of a contrastive learning foundation model for ECG-based prediction of cardiovascular diseases and outcomes.

BACKGROUND: Automated electrocardiogram (ECG) assessment tools to assist clinicians in diagnosis hav...

Electrocardiogram Abnormality Classification From Summary Statistics With Patient-Level Validation and Process-Focused Evaluation.

BACKGROUND: Machine learning (ML) applications in clinical medicine are vulnerable to data leakage, ...