Myocardial Infarction

CCTA without ECG Leads Using Whole-Heart Motion Correction Algorithm Based on Wide-Detector CT.

PURPOSE: This study evaluates the impact of a whole heart motion correction algorithm on image quality and interpretability in coronary computed tomography angiography (CCTA) without ECG leads. MATERIALS AND METHODS: Seventy patients with acute chest pain underwent CCTA without ECG leads examination. Images were reconstructed with standard (STD), coronary vessel tracking motion correction (Snapsho...

From Caffeine to Aspirin: Everyday Molecules as Triggers for Genetically Encoded Proximity Systems.

Chemically induced proximity (CIP) enables programmable control of gene expression, protein activity, cellular signaling, and engineered cell functions using small molecules. Yet many classical CIP platforms rely on ligands that may limit therapeutic translation owing to unfavorable biosafety profiles, off-target activity, incomplete reversibility, dosing constraints, and potential immunogenicity ...

Efficient ECG Representation Learning via Intervention-Based Attribution Alignment.

The widespread adoption of wearable ECG devices has driven an explosive growth of long-term, multi-lead ECG data. However, clinical analysis and model...

Clinical and Echocardiographic Factors Associated With AI-Estimated Atrial Fibrillation Likelihood During Sinus Rhythm in Patients With Documented Paroxysmal Atrial Fibrillation.

BACKGROUND: Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a promising tool for identifying patients with atrial fibrilla...

Deep learning electrocardiogram analysis for rapid rule-out of acute coronary syndrome in the emergency department: a multicenter diagnostic accuracy study.

BACKGROUND: Rapid and accurate exclusion of acute coronary syndrome (ACS) in patients presenting with chest pain remains a major clinical challenge. D...

Large Language Model-Based Localization of Premature Ventricular Contraction Origins: A Retrospective Diagnostic Accuracy Study.

BACKGROUND: Accurate localization of premature ventricular contraction (PVC) origin from 12-lead electrocardiography (ECG) is important for procedural...

Prediction of coronary atherosclerosis progression in type 2 diabetes mellitus based on AI-derived CCTA parameters and clinical factors: a follow-up study.

OBJECTIVE: To develop a predictive model for coronary atherosclerosis progression in patients with type 2 diabetes mellitus (T2DM) based on Artificial...

AI-Enabled ECG Mapping to Guide Leadless Atrial Pacemaker Implantation in the Bachmann's Bundle Region.

BACKGROUND: Physiologic atrial pacing at the Bachmann's bundle region may improve biatrial synchrony, but accurate implantation of atrial leadless pac...

A deep learning framework for multi-lead ECG arrhythmia classification with CAM interpretability and LLM-driven clinical guidance.

Cardiac arrhythmias remain a major cause of morbidity and mortality, requiring accurate and interpretable automated diagnosis. This reserach presents ...

Reconsidering Fibrinolysis in Selected Patients with Non-ST-Elevation Acute Coronary Syndrome: A Hypothesis-Generating, Research-Based Framework.

For decades, the use of fibrinolytic agents in patients with non-ST-elevation acute coronary syndrome (NSTE-ACS) has been contraindicated by major cli...

Preoperative electrocardiography for predicting cardiovascular events after noncardiac surgery: a secondary analysis of two prospective cohorts.

BACKGROUND: The predictive value of preoperative resting ECGs for cardiovascular events after noncardiac surgery is unclear. This study evaluated whet...

Artificial intelligence shows comparable or improved performance to traditional risk models in predicting atrial fibrillation after cryptogenic stroke.

BACKGROUND AND AIMS: Detecting subclinical atrial fibrillation (AF) and initiating anticoagulation therapy are critical for secondary stroke preventio...

Wellens' Sign and Syndrome Detected With the Home Use of a Novel 12-Lead Electrocardiogram.

BACKGROUND: Wellens' sign is a high-risk electrocardiogram (ECG) pattern associated with proximal left anterior descending artery stenoses and high ri...

Stepwise increasing input to machine learning models predicting 30-day AMI or death in emergency department chest-pain patients.

Introduction This study evaluated the performance of neural network (NN) models with stepwise increasing input for identifying acute myocardial infarc...

A lightweight hybrid framework integrating convolutional neural networks and fast Fourier transform for reliable and calibrated ECG-based cardiac abnormality detection.

The electrocardiogram (ECG) is an essential non-invasive tool for detecting cardiac abnormalities; however, accurate interpretation often requires spe...

Risk-Guided Screening for Atrial Fibrillation Using Electronic Health Records.

BACKGROUND: Screening for atrial fibrillation (AF) on the basis of AF risk may be more effective. We aimed to develop, externally validate, and prospe...

ECG-Based Artificial Intelligence for Classifying Left Ventricular Dysfunction and Heart Failure With Preserved Ejection Fraction.

BACKGROUND: Left ventricular (LV) dysfunction and heart failure with preserved ejection fraction (HFpEF) often present with early signs that are frequ...

From Detection to Differentiation: The Evolving Role of Artificial Intelligence-Enabled ECG in Heart Failure Care.

An interpretable machine learning approach to predict left ventricular aneurysm formation following primary PCI in STEMI: A retrospective study with independent external validation.

BACKGROUND: Left ventricular aneurysm (LVA) remains a clinically important structural complication after primary percutaneous coronary intervention (p...

Pilot Evaluation of a Novel Artificial Intelligence (AI) Heart Rate Variability (HRV)-Guided Risk Stratification for Chest Pain in the Emergency Department: A Randomized Controlled Trial.

BACKGROUND: Heart rate variability (HRV) analysis powered by artificial intelligence (AI) offers a rapid, non-invasive, and objective approach for acu...