Arrhythmias

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 settings, tabular electronic health records along with other modalities, such as medical imaging, electrocardiogram data (ECG), and textual doctors' notes and reports. Using deep learning methods, we propose a novel MML approach for mortality prediction in ...

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 cardiovascular events (MACEs). Emerging evidence indicates that metabolic dysregulation substantially influences the AF prognosis. The cardiometabolic index (CMI) and triglyceride-glucose (TyG) index are non-insulin-dependent surrogate markers of metabolic dysf...

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 ...

Atrial Fibrillation Ablation Using 3D Artificial Intelligence Module Integration with Intracardiac Echocardiography.

BACKGROUND: Intracardiac echocardiography (ICE)-based electroanatomical mapping (EAM) improves procedural efficiency and safety in atrial fibrillation...

Multisite, External Validation of an AI-Enabled ECG Algorithm for Detection of Low Ejection Fraction.

BACKGROUND: Low left ventricular ejection fraction (LEF) can progress undiagnosed. Artificial intelligence-based electrocardiogram (ECG-AI) screening ...

Biologically explicable multimodal model predicting local tumor progression and tumor invasiveness of hepatocellular carcinoma.

PURPOSE: Local tumor progression (LTP) of hepatocellular carcinoma (HCC) after thermal ablation (TA) is related to tumor invasiveness and threaten the...

A Dual Classifier-Regressor Architecture for Heart Sound Onset/Offset Detection.

OBJECTIVE: Identifying the first (S1) and second (S2) heart sounds from phonocardiogram (PCG) signals is an essential step in automating the diagnosis...

Multi-window temporal analysis for enhanced arrhythmia classification: leveraging long-range dependencies in electrocardiogram signals.

OBJECTIVE: Arrhythmia classification from electrocardiograms (ECGs) suffers from high false positive rates and limited cross-dataset generalization, p...

High-Strength Degradable Transparent Bio-Electronics Enabled by Confined Crystallization in Aligned Nanofibers for AI-Assisted Motion Sensors.

The severe environmental impact of conventional plastic electronics necessitates next-generation wearable devices simultaneously embodying high perfor...

Low-complexity fetal heart rate monitoring from carbon-based single-channel dry electrodes maternal electrocardiogram.

Objective. Fetal and maternal health during pregnancy can be monitored with sensors such as Doppler or scalp fetal ECG. This study focuses on single-c...

[Artificial intelligence in risk stratification of acute coronary syndrome : Future vision or already reality?].

Artificial intelligence (AI) in cardiology has evolved from rule-based expert systems to data-driven, learning models that can support diagnostic and ...

Comparative study of 11 machine learning algorithms for predicting recurrence risk after atrial fibrillation catheter ablation based on a real-world cohort: a retrospective study.

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia worldwide, with catheter ablation being an effective yet recurrence-prone treatment...

DeeBayes: An interpretable deep Bayesian network for ECG signal restoration.

The electrocardiogram (ECG) is a valuable and non-invasive tool for detecting and preventing arrhythmias. However, in real-world situations, ECG signa...

Old Criteria, New Intelligence: The Evolution of ECG in Pulmonary Hypertension Diagnosis.

BACKGROUND: Pulmonary hypertension (PH) carries a significant mortality risk, highlighting the need for improved early detection strategies. This revi...

External validation of an explainable electrocardiogram-only deep learning algorithm for the prediction of response after cardiac resynchronization therapy.

BACKGROUND: Cardiac resynchronization therapy (CRT) can improve clinical outcomes in patients with dyssynchronous heart failure, but many patients sel...

Machine learning for the prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

BACKGROUND AND OBJECTIVE: This systematic review evaluates the current state of Machine Learning (ML) methods for predicting Atrial Fibrillation (AF) ...

Multi-scale heart simulation augments the explainability of artificial intelligence-enabled electrocardiogram through provision of an electrocardiogram database labelled with cellular pathologies.

BACKGROUND AND OBJECTIVES: Although artificial-intelligence-enhanced electrocardiograms (AI-ECGs) offer prediction and diagnosis capabilities superior...