Cardiovascular

Myocardial Infarction

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

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Showing 190-210 of 6,871 articles
A systematic review and meta-analysis on the performance of convolutional neural networks ECGs in the diagnosis of hypertrophic cardiomyopathy.

INTRODUCTION: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death in younge...

Enhancing cardiovascular disease classification in ECG spectrograms by using multi-branch CNN.

Cardiovascular disease (CVD) is caused by the abnormal functioning of the heart which results in a h...

Analysis of Cardiac Arrhythmias Based on ResNet-ICBAM-2DCNN Dual-Channel Feature Fusion.

Cardiovascular disease (CVD) poses a significant challenge to global health, with cardiac arrhythmia...

Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.

OBJECTIVE: This study aimed to evaluate the predictive performance of inflammatory and nutritional i...

A noninvasive hyperkalemia monitoring system for dialysis patients based on a 1D-CNN model and single-lead ECG from wearable devices.

This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis pat...

Deep learning for the classification of atrial fibrillation using wavelet transform-based visual images.

BACKGROUND: As the incidence and prevalence of Atrial Fibrillation (AF) proliferate worldwide, the c...

Multiscale feature enhanced gating network for atrial fibrillation detection.

BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is a significant cause of life-threatening heart ...

Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery by...

A Deep Learning Approach for Mental Fatigue State Assessment.

This study investigates mental fatigue in sports activities by leveraging deep learning techniques, ...

Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.

BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute...

A Deep and Interpretable Learning Approach for Long-Term ECG Clinical Noise Classification.

OBJECTIVE: In Long-Term Monitoring (LTM), noise significantly impacts the quality of the electrocard...

Development and validation of an explainable machine learning prediction model of hemorrhagic transformation after intravenous thrombolysis in stroke.

OBJECTIVE: To develop and validate an explainable machine learning (ML) model predicting the risk of...

End-to-end deep-learning model for the detection of coronary artery stenosis on coronary CT images.

PURPOSE: We examined whether end-to-end deep-learning models could detect moderate (≥50%) or severe ...

Chlorfenapyr-related delayed rhabdomyolysis: a case series.

INTRODUCTION: Chlorfenapyr, a broad-spectrum insecticide and acaricide of the pyrrole-class pesticid...

Prediction of mortality in intensive care unit with short-term heart rate variability: Machine learning-based analysis of the MIMIC-III database.

BACKGROUND: Prognosis prediction in the intensive care unit (ICU) traditionally relied on physiologi...

rU-Net, Multi-Scale Feature Fusion and Transfer Learning: Unlocking the Potential of Cuffless Blood Pressure Monitoring With PPG and ECG.

This study introduces an innovative deep-learning model for cuffless blood pressure estimation using...

mDARTS: Searching ML-Based ECG Classifiers Against Membership Inference Attacks.

This paper addresses the critical need for elctrocardiogram (ECG) classifier architectures that bala...

Aspirin modulates inflammatory biomarkers in patients with subcortical silent brain infarcts.

INTRODUCTION: This study aimed to identify differences in the levels of inflammation-related biomark...

Prediction of ECG signals from ballistocardiography using deep learning for the unconstrained measurement of heartbeat intervals.

We developed a deep learning-based extraction of electrocardiographic (ECG) waves from ballistocardi...

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