Arrhythmias

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 ejection fraction (LVEF) in patients with systolic dysfunction, especially in suspected PVC-induced cardiomyopathy. However, many patients fail to normalize LVEF despite successful ablation, and current tools do not reliably distinguish true PVC-induced cardiomyopathy from underlying cardiomyopathy...

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 dysfunction, particularly among critically ill an...

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 prevalence estimates of approximately 30%. Current noninva...

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 caused by acoustic aberrations for real-time treatmen...

New Evidence in Heart Failure: 2026 Update.

Heart failure (HF) remains a major cause of morbidity, mortality, impaired quality of life and healthcare expenditure worldwide. The global burden of ...

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 have improved substantially over the past decade; how...

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, particularly temporal leakage from post-diagnostic...

Artificial Intelligence in Echocardiography for Acute Coronary Syndromes: Current Evidence and Integration With Electrocardiography and Biomarkers.

BACKGROUND: Early diagnosis of acute coronary syndromes (ACS) remains challenging because electrical, mechanical, and biochemical manifestations of my...

Advanced detection of fetal arrhythmia utilizing spatial deep convolutional neural network with GHA-DenseNet for enhanced electrocardiographic signal processing.

Fetal arrhythmia is a critical medical condition associated with perinatal morbidity and cardiac complications. This paper proposes an Advanced Detect...

Prognostic Value of Artificial Intelligence ECG-Derived Diastolic Function in Surgical Aortic Valve Replacement.

BACKGROUND: Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. W...

Classification of intracardiac heart sounds for ventricular and atrial fibrillation based on feature fusion and voting ensemble learning.

Arrhythmias, a significant category of cardiovascular diseases, including atrial fibrillation (AF) and ventricular fibrillation (VF), can result in se...

Ai-based multimodal analysis of ECG and clinical data for evaluation for competitive sports participation: The VALETUDO trial.

AIMS: Pre-participation cardiovascular screening (PPS) is essential for preventing SCD in athletes, yet ECG interpretation requires expertise and rema...

Artificial intelligence-based ECG as a triage tool for acute myocardial infarction: a diagnostic systematic review and meta-analysis.

Early identification of acute myocardial infarction (AMI) remains challenging, particularly in non-ST-segment elevation presentations and occluded myo...

Myocardial Fibrosis in Pediatric Heart Disease: Mechanisms, Imaging Biomarkers, Clinical Consequences, and Emerging Antifibrotic Therapies.

Myocardial fibrosis is increasingly recognized as a central determinant of adverse cardiac remodeling and long-term cardiovascular outcomes in childre...

Electrocardiogram-Based Deep Learning to Prioritize Testing for Transthyretin Amyloid Cardiomyopathy.

IMPORTANCE: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a treatable cause of heart failure, but diagnosis is often delayed. Accessible tools to ...

When multimodal fusion helps: An ablation study of EEG-ECG fusion strategies for emotion recognition.

Multimodal physiological signal fusion-particularly electroencephalography (EEG) and electrocardiography (ECG)-is widely assumed to improve emotion re...

Subtype specific artificial intelligence modelling of cardiac amyloidosis using echocardiography and electrocardiography.

AIMS: We aimed to develop and validate echocardiography-based prediction models for light chain (AL) and transthyretin (ATTR) cardiac amyloidosis and ...

Personalized ECG-based artificial intelligence models for early seizure prediction.

Seizure prediction is critically important, as it can help prevent serious injuries, improve quality of life, and potentially reduce the risk of SUDEP...

MRI radiomics combined with clinical features for predicting initial dose in focused ultrasound ablation surgery for uterine fibroids.

OBJECTIVE: To develop and validate a radiomics-clinical model for individualized prediction of the initial treatment dose in focused ultrasound ablati...