Cardiovascular

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

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Right Ventricular Strain as a Key Feature in Interpretable Machine Learning for Identification of Takotsubo Syndrome: A Multicenter CMR-based Study.

RATIONALE AND OBJECTIVES: To develop an interpretable machine learning (ML) model based on cardiac m...

Interpretable machine learning prediction model for major adverse cardiovascular events in patients with peripheral artery disease.

BACKGROUND: Major adverse cardiovascular events (MACEs) are severe complications of peripheral arter...

Association between atherogenicity indices and prediabetes: a 5-year retrospective cohort study in a general Chinese physical examination population.

BACKGROUND AND OBJECTIVE: Atherogenicity indices have emerged as promising markers for cardiometabol...

A machine learning model using echocardiographic myocardial strain to detect myocardial ischemia.

Coronary functional assessment plays a critical role in guiding decisions regarding coronary revascu...

Making Sense of Missense: Benchmarking MutScore for Variant Interpretation in Inherited Cardiac Diseases.

BACKGROUND: Accurate interpretation of genetic variants still represents a major challenge. Accordin...

AI-enhanced computational discovery of promising ALK5 inhibitors in a ultra-large chemical space library for cardiovascular Disease therapy.

Cardiac fibrosis, characterized by excessive extracellular matrix deposition, is a critical contribu...

Cerebral ischemia detection using deep learning techniques.

Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to con...

Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training.

This study explores nursing students' stress responses while they are being trained in a mixed reali...

A Talk with ChatGPT: The Role of Artificial Intelligence in Shaping the Future of Cardiology and Electrophysiology.

: Artificial intelligence (AI) is poised to significantly impact the future of cardiology and electr...

"ShapeNet": A Shape Regression Convolutional Neural Network Ensemble Applied to the Segmentation of the Left Ventricle in Echocardiography.

Left ventricle (LV) segmentation is crucial for cardiac diagnosis but remains challenging in echocar...

Trading off Iodine and Radiation Dose in Coronary Computed Tomography.

Coronary CT angiography (CCTA) has seen steady progress since its inception, becoming a key player i...

Artificial intelligence and the electrocardiogram: A modern renaissance.

Integrating Artificial Intelligence (AI) with electrocardiograms (ECG) represents a transformative s...

Atrial fibrillation detection via contactless radio monitoring and knowledge transfer.

Atrial fibrillation (AF) has been a prevalent and serious arrhythmia associated with increased morbi...

Using Machine Learning to Predict MACEs Risk in Patients with Premature Myocardial Infarction.

BACKGROUND: The study aimed to develop an interpretable machine learning (ML) model to assess and st...

A novel method of BiFormer with temporal-spatial characteristics for ECG-based PVC detection.

INTRODUCTION: Premature Ventricular Contractions (PVCs) can be warning signs for serious cardiac con...

Risk prediction of stroke-associated pneumonia in acute ischemic stroke with atrial fibrillation using machine learning models.

Stroke-associated pneumonia (SAP) is a serious complication of acute ischemic stroke (AIS), signific...

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