Arrhythmias

CEUS-Based Microvascular Invasion Predictor in HCC: Improving Prognostic Stratification Following Thermal Ablation.

BACKGROUND & AIMS: Microvascular invasion (MVI) critically impacts hepatocellular carcinoma (HCC) management. We aimed to develop and validate a deep learning model integrating contrast-enhanced ultrasound (CEUS) and clinical features for assessing MVI risk preoperatively, and to explore its prognostic associations in the thermal ablation (TA) cohort. METHODS: We enrolled 688 patients with solitar...

AI-enhanced cardiac digital twins extend drug proarrhythmic risk assessment through experimental data uncertainty propagation and overdose exploration: A loperamide case study.

Drug-induced QT interval prolongation is a key biomarker of proarrhythmic risk and central to drug cardiac safety evaluation alongside in vitro assays and animal studies, yet current preclinical frameworks provide limited insight into how experimental uncertainty and extreme exposures translate into real-world arrhythmic risk despite both factors critically modulating outcomes. To address this, we...

Long-term continuous monitoring of wearable ECG signals for atrial fibrillation detection.

Atrial Fibrillation (AFib) is the most common sustained cardiac arrhythmia and is associated with substantial morbidity and mortality, including incre...

MRI-based deep learning radiomics model for automated classification of disc degeneration in the lumbar spine.

Disc degeneration in the lumbar spine is a major cause of low back pain (LBP). The accurate grading of disc degeneration on magnetic resonance imaging...

Generalisable artificial intelligence ECG trained on public data for outcome prediction after transcatheter aortic valve replacement.

BACKGROUND: Artificial intelligence ECG (AI-ECG) models can predict cardiovascular outcomes, but their clinical adoption is limited by restricted acce...

CAHA-Net: A novel MR image classification model based on DenseNet incorporating coordinate attention and hybrid augmentation.

Traditional diagnostic approaches are time-consuming and labor-intensive, and the field currently lacks a comprehensive evaluation of mainstream model...

Cardiac arrhythmia detection via PQRST analyzed data using an optimized hierarchical fused fuzzy deep reinforcement learning.

Cardiac arrhythmia is a disorder caused by disruptions in the regular heart rhythm. Arrhythmias are categorized into two classes: sinus and non-sinus ...

Fully Biobased, Robust, and High-Conductivity Hydrogel for High-Fidelity Electrophysiological Monitoring and Deep Learning-Assisted Stroke Rehabilitation.

Developing sustainable bioelectronics that simultaneously integrate mechanical robustness, high conductivity, biocompatibility, and system-level funct...

Arrhythmia Burden and Clinical Responses Under Continuous Monitoring in Heart Failure: Observations From the ALLEVIATE-HF Trial.

BACKGROUND: Arrhythmia burden in ambulatory patients with symptomatic heart failure (HF) without cardiac implantable electronic devices (CIEDs) is not...

Detection of Microbehavior Intervals for Predicting Mental Health: Clinically Relevant and Advanced Multimodal Temporal Analysis.

BACKGROUND: Health care workers (HCWs) face sustained psychological demands that place them at heightened risk for burnout and posttraumatic stress di...

An enhanced hypergraph CNN with adaptive focal loss for automated ECG heartbeat classification.

Deep learning techniques have shown significant promise for the automated diagnosis of CVD using ECG analysis. Nevertheless, several critical challeng...

AI in the EP Lab - Mapping, Imaging, and Signal Interpretation.

Artificial intelligence (AI) is the use of computational models to learn from electrical, anatomical and imaging data to assist or automate interpreta...

Predicting immune checkpoint inhibitor cardiotoxicity using machine learning: a systematic review of model performance and methodological quality.

BACKGROUND: Immune checkpoint inhibitors (ICIs) significantly improve cancer outcomes but can cause rare, potentially fatal cardiotoxicity, including ...

A multi-branch vision transformer with GA-optimized focal loss for ECG signal classification.

Background The classification of electrocardiogram (ECG) signals is a critical task in detecting cardiac arrhythmias. However, challenges such as clas...

Prediction of Neurological Outcome and Mortality in Cardiac Arrest Patients: An Explainable Machine Learning Study Integrating HRV, EEG, and Clinical Features.

OBJECTIVE: Early and accurate prediction of neurological outcomes and mortality in comatose patients after cardiac arrest remains challenging. Multimo...

CODE-II: a large-scale dataset for artificial intelligence in ECG analysis.

Data-driven methods for electrocardiogram (ECG) interpretation are rapidly progressing. Large datasets have enabled advances in artificial intelligenc...

DNA demethylation of ANXA4 is associated with atrial fibrillation risk through myeloid immune mechanisms: evidence from Mendelian randomization and multi-omics analyses.

BACKGROUND: Atrial fibrillation (AF) is a common arrhythmia affecting millions of patients globally. While epigenetic modifications play a significant...

Endurance exercise remodels pulmonary vein sleeve myocytes and promotes a proarrhythmic atrial substrate.

BACKGROUND AND AIMS: The risk of atrial fibrillation (AF) is higher in endurance athletes. Pulmonary vein isolation (PVI) is effective in this group, ...

Advances in machine learning for cardiac event prediction: where can we still improve?

INTRODUCTION: Artificial intelligence (AI) is playing a transformative role in cardiovascular care by enabling more precise prediction of adverse clin...

Beyond Terminator Narratives: Implantable Cardioverter-Defibrillators as a Lens for Clinical Agentic Artificial Intelligence.