Myocardial Infarction

Domain Knowledge is Power: Leveraging Physiological Priors for Self-Supervised Representation Learning in Electrocardiography.

Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG analysis is often hindered by the limited availability of labeled data. Self-supervised learning (SSL) can address this by leveraging large-scale unlabeled data. We introduce PhysioCLR (Physiology-aware Contrastive Learning Representation for ECG), a ph...

Data-driven pediatric ECG reference intervals with VSD-based validation.

To establish population-specific, age- and sex-stratified electrocardiographic (ECG) reference ranges for Chinese children and adolescents using a data-driven approach, addressing the limitations of conventional empirically defined age groupings. Approach. A total of 35,088 ECG recordings from individuals under 18 years of age without structural heart disease or electrocardiographic abnormalit...

A hybrid hierarchical transformer model for ECG classification and age prediction.

Electrocardiograms (ECGs) play a crucial role in cardiovascular healthcare, requiring effective analytical models. ECG analysis is inherently hierarch...

Artificial Intelligence-Enhanced Electrocardiogram Models for Detection of Left Ventricular Dysfunction: A Comparison Study.

BACKGROUND: Several artificial intelligence-enhanced electrocardiogram (AI-ECG) models have shown promise in detecting left ventricular systolic dysfu...

[Evaluation of an interpretable 12-lead ECG automatic diagnosis model based on deep feature fusion].

OBJECTIVES: To enhance the accuracy and reliability of 12-lead electrocardiogram (ECG) automatic diagnosis. METHODS: Herein we propose a 12-lead ECG a...

Mapping Algorithmic Bias in AI-Powered Electrocardiogram Interpretation Across the AI Life Cycle: Protocol for a Scoping Review.

BACKGROUND: Artificial intelligence (AI)-powered analysis of electrocardiograms (ECGs) is reshaping cardiac diagnostics, offering faster and often mor...

[Potential of imaging in the clinical routine : Focus on cardiovascular MRI].

Cardiac magnetic resonance imaging (CMR) provides decisive advantages, particularly in coronary heart disease, myocarditis and cardiomyopathy. It accu...

uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm.

INTRODUCTION: Premature Ventricular Contractions (PVCs) are common cardiac arrhythmias originating from the ventricles. Accurate detection remains cha...

Quantifying Innovation in Stroke: Large Language Model Bibliometric Analysis.

BACKGROUND: Thrombolysis and mechanical thrombectomy represent the most successful stroke innovations over the last 30 years. Quantifying innovation i...

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 setting...

Lesion stratification with intracoronary imaging.

Intracoronary (IC) imaging-guided percutaneous coronary intervention (PCI) improves clinical outcomes in patients with high clinical and anatomical ri...

OCT-Derived Virtual Fractional Flow Reserve Associated With 1-Year Outcomes After PCI in ACS Patients.

BACKGROUND: Despite PCI, many acute coronary syndrome (ACS) patients experience major adverse cardiovascular events (MACE). Angiography is limited, an...

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 ...

Predicting functional results of percutaneous coronary intervention using machine learning modelling.

BACKGROUND: Post-percutaneous coronary intervention (PCI) Murray's law-based quantitative flow ratio (μFR) is associated with long-term clinical outco...

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 ...

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...

Artificial intelligence based retinal imaging for cardiovascular risk and statin guidance in retinal vein occlusion.

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...