Cardiovascular

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

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Integration of Cine-cardiac Magnetic Resonance Radiomics and Machine Learning for Differentiating Ischemic and Dilated Cardiomyopathy.

RATIONALE AND OBJECTIVES: This study aims to evaluate the capability of machine learning algorithms ...

LUNet: deep learning for the segmentation of arterioles and venules in high resolution fundus images.

This study aims to automate the segmentation of retinal arterioles and venules (A/V) from digital fu...

Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.

Almost 35 years after its introduction, coronary artery calcium score (CACS) not only survived techn...

Exploring machine learning strategies for predicting cardiovascular disease risk factors from multi-omic data.

BACKGROUND: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular di...

Deep Learning Synthesis of White-Blood From Dark-Blood Late Gadolinium Enhancement Cardiac Magnetic Resonance.

OBJECTIVES: Dark-blood late gadolinium enhancement (DB-LGE) cardiac magnetic resonance has been prop...

An Artificial Intelligence-Driven Approach for Automatic Evaluation of Right-to-Left Shunt Grades in Saline-Contrasted Transthoracic Echocardiography.

BACKGROUND: Intracardiac or pulmonary right-to-left shunt (RLS) is a common cardiac anomaly associat...

Development of a Predictive Model for Survival Over Time in Patients With Out-of-Hospital Cardiac Arrest Using Ensemble-Based Machine Learning.

As of now, a model for predicting the survival of patients with out-of-hospital cardiac arrest has n...

Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.

Heart failure (HF) is a leading cause of morbidity and mortality in the United States and worldwide,...

Automatic assessment of DWI-ASPECTS for acute ischemic stroke based on deep learning.

BACKGROUND: Alberta Stroke Program Early Computed Tomography Score (ASPECTS) is a standardized semi-...

Prediction and causal inference of hyperuricemia using gut microbiota.

Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as...

A Case Report of Becker Muscular Dystrophy and Stroke Who Successfully Regained Mobility With Robot-Assisted Gait Training.

A 30-yr-old patient with Becker muscular dystrophy presented with stroke. Background issues of proxi...

Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence.

OBJECT: The aim of this study was at building an effective machine learning model to contribute to t...

Motion correction and super-resolution for multi-slice cardiac magnetic resonance imaging via an end-to-end deep learning approach.

Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive ca...

Improved hypertensive stroke classification based on multi-scale feature fusion of head axial CT angiogram and multimodal learning.

PURPOSE: Strokes are severe cardiovascular and circulatory diseases with two main types: ischemic an...

Preclinical identification of acute coronary syndrome without high sensitivity troponin assays using machine learning algorithms.

Preclinical management of patients with acute chest pain and their identification as candidates for ...

Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs.

BACKGROUND: Sinus node dysfunction because of abnormal impulse generation or sinoatrial conduction b...

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardi...

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