Cardiovascular

Latest AI and machine learning research in cardiovascular for healthcare professionals.

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Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.

One of the most common causes of death worldwide is heart disease, including arrhythmia. Today, scie...

Heart patient health monitoring system using invasive and non-invasive measurement.

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry th...

Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data.

In the current study, we demonstrate the use of a quality framework to review the process for improv...

3D printing of an artificial intelligence-generated patient-specific coronary artery segmentation in a support bath.

Accurate segmentation of coronary artery tree and personalized 3D printing from medical images is es...

Artificial Intelligence-Derived Risk Prediction: A Novel Risk Calculator Using Office and Ambulatory Blood Pressure.

BACKGROUND: Quantification of total cardiovascular risk is essential for individualizing hypertensio...

Deep learning models for ischemic stroke lesion segmentation in medical images: A survey.

This paper provides a comprehensive review of deep learning models for ischemic stroke lesion segmen...

Prediction of coronary artery bypass graft outcomes using a single surgical note: An artificial intelligence-based prediction model study.

BACKGROUND: Healthcare providers currently calculate risk of the composite outcome of morbidity or m...

A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images.

ObjectivesAssessment of plaque stenosis severity allows better management of carotid source of strok...

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes.

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particu...

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.

AIMS: Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused...

Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho...

Temporal Relationship-Aware Treadmill Exercise Test Analysis Network for Coronary Artery Disease Diagnosis.

The treadmill exercise test (TET) serves as a non-invasive method for the diagnosis of coronary arte...

Machine learning approach for prediction of outcomes in anticoagulated patients with atrial fibrillation.

BACKGROUND: The accuracy of available prediction tools for clinical outcomes in patients with atrial...

Integrating machine learning algorithms and single-cell analysis to identify gut microbiota-related macrophage biomarkers in atherosclerotic plaques.

OBJECTIVE: The relationship between macrophages and the gut microbiota in patients with atherosclero...

How robot-assisted gait training affects gait ability, balance and kinematic parameters after stroke: a systematic review and meta-analysis.

INTRODUCTION: Gait ability is often cited by stroke survivors. Robot-assisted gait training (RAGT) c...

Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration.

Deep neural networks must address the dual challenge of delivering high-accuracy predictions and pro...

AlpaPICO: Extraction of PICO frames from clinical trial documents using LLMs.

In recent years, there has been a surge in the publication of clinical trial reports, making it chal...

Portable robots for upper-limb rehabilitation after stroke: a systematic review and meta-analysis.

BACKGROUND: Robot-assisted upper-limb rehabilitation has been studied for many years, with many rand...

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