Myocardial Infarction

Mechanics, Fracture, and Fragmentation of Thrombi: A Computational Study Review.

Thrombosis drives myocardial infarction, acute ischemic stroke and pulmonary embolism, yet more than half of patients undergoing mechanical thrombectomy fail to achieve functional independence, and residual thrombus is detected in up to 91% of cases after catheter-directed thrombolysis. These persistent clinical failures reflect a fundamental gap: thrombus removal is not merely dissolution or extr...

Machine learning-based identification of cardiovascular risk modifiers in patients with very high LDL cholesterol: A cross-sectional study.

Individuals with LDL cholesterol (LDL-C) ≥ 190 mg/dL face a markedly increased risk of major adverse cardiovascular events (MACE). Traditional risk calculators such as SCORE2 and the Framingham risk score often underestimate this risk. In this study, we aimed to apply machine learning techniques to identify clinical and laboratory features associated with the presence of MACE among patients with v...

Robust stress detection from wearable ECG using subject-specific normalization and stacking ensemble learning.

Driven by the rising prevalence of mental health issues, this study proposes an automated stress monitoring system using wearable ECG signals. Designe...

R-R Interval Histogram-Based Deep Learning for 3-Class Atrial Fibrillation Screening in Garment-Type Wearable Holter Electrocardiogram Monitoring: Algorithm Development and Validation Study.

BACKGROUND: Long-term garment-type wearable Holter electrocardiographic (ECG) monitoring is frequently affected by noise contamination, which complica...

Direct Reconstruction of High-Fidelity Electrocardiogram Signals From Vector-Based PDF Files With Integrated Deep Learning for Multiparameter Estimation: Retrospective Methodological Study.

BACKGROUND: Electrocardiograms (ECGs) are commonly stored in PDF, particularly as vector-based files generated by ECG management systems. Previous stu...

Leipzig Heart Center ECG-database: Arrhythmias in children and patients with congenital heart disease.

Interpretation of Electrocardiogramms (ECG) is increasingly complemented by algorithms. These algorithms are based on large datasets. Here we pre...

First-pass aspiration versus stent retriever thrombectomy: machine learning study of treatment effect heterogeneity.

BACKGROUND: Randomized trials have shown equivocal outcomes between a direct aspiration as first-pass technique (ADAPT) and stent retriever (SR) strok...

AI-ECG Risk Stratification for Atrial Fibrillation: Real-World Performance and Explainability.

BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a potential method for identifying atrial fibrillation (AF) fr...

Post-thrombectomy models for outcome prediction in ischemic stroke: systematic review and external validation.

BACKGROUND: Outcome prediction models for patients with ischemic stroke after endovascular thrombectomy (EVT) demonstrated the value of including post...

Role of Low-Field MRI in Acute Stroke.

Portable, low-field (LF) magnetic resonance imaging (MRI) is emerging as a clinically relevant adjunct in acute stroke care, enabling MRI in environme...

Morphology-preserving transfer-guided GAN with adaptive discriminative loss for robust and imbalanced ECG arrhythmia detection.

Electrocardiogram (ECG) based arrhythmia detection remains challenging due to severe class imbalance, morphological variability, and noise commonly pr...

A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients.

OBJECTIVE: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse heal...

HPA-Net: a lightweight hierarchical progressive fusion network for cardiac disease detection based on ECG-PCG signals.

Cardiovascular disease (CVD) remains a leading global health threat, creating a strong clinical need for convenient and rapid diagnostic methods. Howe...

Design and Analysis of Emotion Elicitation Techniques for ECG-Based Emotion Recognition in Children with Autism Spectrum Disorder (ASD).

Children with autism spectrum disorder (ASD) face difficulties in expressing and recognizing emotions resulting in meltdowns and aggressive situations...

Stage-specific predictors of exercise adherence after percutaneous coronary intervention: a prospective multicenter study.

BACKGROUND: The efficacy of exercise after Percutaneous Coronary Intervention (PCI) is compromised by poor long-term adherence. Although multiple infl...

Precision Medicine for Anticoagulation Strategies in the Cath Lab: Part 2.

PURPOSE OF REVIEW: Intraprocedural anticoagulation during percutaneous coronary intervention (PCI) remains particularly challenging in high-risk and u...

Changes in collateral status during transfer for thrombectomy are predictive of functional outcome in large vessel occlusion stroke.

Collateral circulation that determines infarct progression in large vessel occlusion (LVO) is implicitly regarded as stationary. We investigated colla...

Development and external validation of AI-ECG models in athlete pre-participation screening: Performance, limitations, and clinical implications.

BACKGROUND: Pre-participation screening (PPS) in competitive athletes aims to identify cardiovascular diseases associated with sudden cardiac death (S...

Understanding HFpEF Through Imaging: Strengths, Limitations, and Current State of the Art.

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterized by dyspnea caused by hemodynamic congestion, which develop...

Natural language processing identifies symptoms predicting complete coronary artery occlusion in patients with NSTEMI.

AIMS: One in 10 patients present to the emergency department (ED) with symptoms of acute coronary syndrome (ACS). The 13-item ACS Symptom Checklist is...