Myocardial Infarction

Identification and validation of cholesterol homeostasis and NK cell-associated predictive and therapeutic models for Coronary atherosclerotic heart disease patients with ST-elevated myocardial infarction: Insights from artificial intelligence and multi-omics.

OBJECTIVE: Dysregulation of Cholesterol homeostasis(CH) and NK cells proportion can increase risk of ST-Elevated Myocardial Infarction(STEMI) for Coronary atherosclerotic heart disease(CAD) patients. Hence, it is necessary for the investigation of CH and MC in pathogenesis of STEMI for CAD patients, providing additional choice for the prevention of STEMI for CAD patients. METHODS: By combining 5 p...

Development and Validation of a Diffusion-weighted Imaging-only Deep Learning Model for the Detection of Supratentorial Acute Ischemic Infarcts: A Retrospective Diagnostic Accuracy Study.

BACKGROUND: Acute ischemic stroke is a leading cause of death and long-term disability worldwide, with a disproportionately increasing burden in low- and middle-income countries. Diffusion-weighted imaging (DWI) is the most sensitive magnetic resonance imaging (MRI) sequence for the early detection of acute ischemia, as treatment decisions, including intravenous thrombolysis and mechanical thrombe...

Generative AI for ECG Interpretation Education: Impact on Nursing, Student Performance, and AI Model Accuracy.

BACKGROUND: Electrocardiogram (ECG) interpretation is a critical yet challenging skill for nurses. Generative artificial intelligence (AI) offers pote...

Machine learning models for outcome prediction of patients with ischaemic stroke undergoing reperfusion therapy: a systematic review and meta-analysis.

BACKGROUND: Reperfusion therapy, including thrombolysis and thrombectomy, is crucial for ischaemic stroke treatment. However, patient outcomes often r...

ECG Signatures and Long-Term Ischemic Stroke Risk: A Deep Learning Analysis of 200,000 Patients.

BACKGROUND: Scalable risk stratification for ischemic stroke remains an unmet need. OBJECTIVES: In this study, the authors sought to assess whether de...

Diagnostic Performance Evaluation of Clinical and AI Risk Models in Patients Referred for Cardiac Amyloidosis Testing.

BACKGROUND: To improve screening for cardiac amyloidosis (CA), several models using artificial intelligence (AI) and conventional statistics have been...

A deep learning ECG model for identification and localization of occlusion myocardial infarction.

Rapid identification and localization of an acute coronary occlusion are vital to prevent myocardial damage, yet reliance on ST-segment ECG criteria m...

Detecting cardiovascular diseases using ECG scans and explainable artificial intelligence.

BACKGROUND AND OBJECTIVE: Cardiovascular diseases are the leading cause of mortality globally, requiring early and accurate detection through tools li...

Myocardial relaxation, left ventricular filling, and exercise capacity in older subjects with dyspnea.

With aging, left ventricular (LV) early diastolic lengthening declines. Delayed or dyssynchronous untwisting and relaxation may slow and reduce fillin...

Stacking Deep Neural Networks to Detect Multiple Types of Cardiac Arrhythmias.

Heart arrhythmias are associated with serious cardiovascular diseases and can result in fatal outcomes if not diagnosed early. Electrocardiograms (ECG...

Artificial Intelligence-Enhanced Electrocardiography and Health Records to Predict Cardiac Arrest.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is a public health burden with the majority occurring in the general population for whom there is no...

Minority class-aware multiclass arrhythmia detection using conditional GAN augmentation.

The detection of cardiac arrhythmias from electrocardiogram (ECG) signals is essential for preventing sudden cardiac deaths. However, model performanc...

Atrial repolarization wave: a new approach using spline-based feature engineering and explainable AI for atrial arrhythmia diagnosis.

OBJECTIVES: The atrial repolarization (Ta wave) characteristics remains largely unexplored, given its inherently low amplitude and obscured by the QRS...

Unveiling the Microvasculature: The Index of Microcirculatory Resistance and Its Expanding Role in Cardiovascular Care.

Index of microcirculatory resistance (IMR) is a cutting-edge, wire-based tool that advances the capability assessment of coronary microvascular functi...

Clinician engagement shapes the impact of AI-based ECG screening for chronic liver disease in primary care.

Artificial intelligence (AI)-based screening tools show promise for early identification of chronic liver disease (CLD), yet their effectiveness in re...

Novel machine learning fusion architectures integrating electrocardiogram representations: applications to acute coronary event detection.

AIMS: AI in electrocardiography (ECG) has diverged into two paths: traditional signal processing with machine learning, and deep learning of raw wavef...

Development of Models for Predicting the 7-Month Risk of Venous Thromboembolism and Clinically Relevant Bleeding in Ambulatory Patients with Cancer: Analysis from the AVERT Trial.

BACKGROUND: Patients with cancer are at elevated risk of venous thromboembolism (VTE). While primary thromboprophylaxis reduces VTE incidence, it also...

In Situ Formation of Mechanical Interlocking Skin and Electronics Interfaces through Dual-Phase Transition of Conductive Materials.

The increasing demand for daily health monitoring has accelerated the development of wearable devices. However, conventional wearables, typically base...

Incremental Prognostic Power of Coronary Flow Reserve and Artificial Intelligence-Enabled ECG-Derived Filling Pressure in Angina With Nonobstructive Coronary Arteries.

BACKGROUND: Coronary flow reserve reflects microvascular function, whereas filling pressure indicates myocardial hemodynamic burden. In angina with no...

Artificial Intelligence Electrocardiogram and Left Ventricular Systolic Dysfunction in Kenya.

IMPORTANCE: Early detection of risk of heart failure with reduced ejection fraction remains challenging in resource-limited settings due to limited ac...