Cardiovascular

Myocardial Infarction

Latest AI and machine learning research in myocardial infarction for healthcare professionals.

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Showing 421-441 of 6,871 articles
Inverse radon transform with deep learning: an application in cardiac motion correction.

. This paper addresses performing inverse radon transform (IRT) with artificial neural network (ANN)...

Automatic prediction of obstructive sleep apnea event using deep learning algorithm based on ECG and thoracic movement signals.

BACKGROUND: Obstructive sleep apnea (OSA) is a sleeping disorder that can cause multiple complicatio...

A novel deep learning approach for early detection of cardiovascular diseases from ECG signals.

Cardiovascular diseases, often asymptomatic until severe, pose a significant challenge in medical di...

Incidence, Determinants, and Outcome of Contrast-induced Acute Kidney Injury following Percutaneous Coronary Intervention at a Tertiary Care Hospital.

Contrast-induced acute kidney injury (CI-AKI) after percutaneous coronary intervention (PCI) is the ...

Heart failure classification using deep learning to extract spatiotemporal features from ECG.

BACKGROUND: Heart failure is a syndrome with complex clinical manifestations. Due to increasing popu...

Quest for the ideal assessment of electrical ventricular dyssynchrony in cardiac resynchronization therapy.

This paper reviews the literature on assessing electrical dyssynchrony for patient selection in card...

Model-based estimation of AV-nodal refractory period and conduction delay trends from ECG.

Atrial fibrillation (AF) is the most common arrhythmia, associated with significant burdens to pati...

Advancing Cardiovascular Risk Assessment with Artificial Intelligence: Opportunities and Implications in North Carolina.

Cardiovascular disease mortality is increasing in North Carolina with persistent inequality by race,...

ECG arrhythmia detection in an inter-patient setting using Fourier decomposition and machine learning.

ECG beat classification or arrhythmia detection through artificial intelligence (AI) is an active to...

Multichannel high noise level ECG denoising based on adversarial deep learning.

This paper proposes a denoising method based on an adversarial deep learning approach for the post-p...

Pre-Processing techniques and artificial intelligence algorithms for electrocardiogram (ECG) signals analysis: A comprehensive review.

Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart's ele...

Microneedle-Assisted Transfersomes as a Transdermal Delivery System for Aspirin.

Transdermal drug delivery systems offer several advantages over conventional oral or hypodermic admi...

Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients.

The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is eit...

Contrast-Induced Nephropathy in Interventional Cardiology: Incidence, Risk Factors, and Identification of High-Risk Patients.

AIM: This study aimed to study contrast-induced nephropathy (CIN) or more recent nomenclature contra...

Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm.

BACKGROUND: Multiple smart devices capable of automatically detecting atrial fibrillation (AF) based...

Machine Learning Insights Into Uric Acid Elevation With Thiazide Therapy Commencement and Intensification.

Background Elevated serum uric acid, associated with cardiovascular conditions such as atherosclerot...

Comparison of Machine Learning Detection of Low Left Ventricular Ejection Fraction Using Individual ECG Leads.

The 12-lead electrocardiogram (ECG) is the most common front-line diagnosis tool for assessing cardi...

Validation of an automated artificial intelligence system for 12‑lead ECG interpretation.

BACKGROUND: The electrocardiogram (ECG) is one of the most accessible and comprehensive diagnostic t...

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.

BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes th...

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