Cardiovascular

Echocardiographic Detection of Regional Wall Motion Abnormalities Using Artificial Intelligence Compared to Human Readers.

BACKGROUND: Although regional wall motion abnormality (RWMA) detection is foundational to transthora...

Evaluating convolutional neural network-enhanced electrocardiography for hypertrophic cardiomyopathy detection in a specialized cardiovascular setting.

The efficacy of convolutional neural network (CNN)-enhanced electrocardiography (ECG) in detecting h...

Diastolic function assessment with four-dimensional flow cardiovascular magnetic resonance using automatic deep learning E/A ratio analysis.

BACKGROUND: Diastolic left ventricular (LV) dysfunction is a powerful contributor to the symptoms an...

Using machine learning to predict outcomes of patients with blunt traumatic aortic injuries.

BACKGROUND: The optimal management of blunt thoracic aortic injury (BTAI) remains controversial, wit...

1-Year Mortality Prediction through Artificial Intelligence Using Hemodynamic Trace Analysis among Patients with ST Elevation Myocardial Infarction.

: Patients presenting with ST Elevation Myocardial Infarction (STEMI) due to occlusive coronary arte...

Mechanistic Assessment of Cardiovascular State Informed by Vibroacoustic Sensors.

Monitoring blood pressure, a parameter closely related to cardiovascular activity, can help predict ...

Myocardial scar and left ventricular ejection fraction classification for electrocardiography image using multi-task deep learning.

Myocardial scar (MS) and left ventricular ejection fraction (LVEF) are vital cardiovascular paramete...

PRERISK: A Personalized, Artificial Intelligence-Based and Statistically-Based Stroke Recurrence Predictor for Recurrent Stroke.

BACKGROUND: Predicting stroke recurrence for individual patients is difficult, but individualized pr...

Validation of a fully automated deep learning-enabled solution for CCTA atherosclerotic plaque and stenosis quantification in a diverse real-world cohort.

Machine learning in risk prediction of continuous renal replacement therapy after coronary artery bypass grafting surgery in patients.

OBJECTIVES: This study aimed to develop machine learning models for risk prediction of continuous re...

Deep Learning-Derived Myocardial Strain.

BACKGROUND: Echocardiographic strain measurements require extensive operator experience and have sig...

Machine Learning Identifies Higher Survival Profile In Extracorporeal Cardiopulmonary Resuscitation.

OBJECTIVES: Extracorporeal cardiopulmonary resuscitation (ECPR) has been shown to improve neurologic...

Diagnostic and prognostic performance of artificial intelligence-based fully-automated on-site CT-FFR in patients with CAD.

Currently, clinically available coronary CT angiography (CCTA) derived fractional flow reserve (CT-F...

Ultra-sensitive analysis of exhaled biomarkers in ozone-exposed mice via PAI-TOFMS assisted with machine learning algorithms.

Ground-level ozone ranks sixth among common air pollutants. It worsens lung diseases like asthma, em...

The future of valvular heart disease assessment and therapy.

Valvular heart disease (VHD) is becoming more prevalent in an ageing population, leading to challeng...

Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data.

The detection of spontaneous magnetic signals can be used for the non-invasive electrophysiological ...

Artificial Intelligence for Identification of Images with Active Bleeding in Mesenteric and Celiac Arteries Angiography.

PURPOSE: The purpose of this study is to evaluate the efficacy of an artificial intelligence (AI) mo...

Deep Learning to Estimate Cardiovascular Risk From Chest Radiographs : A Risk Prediction Study.

BACKGROUND: Guidelines for primary prevention of atherosclerotic cardiovascular disease (ASCVD) reco...

Prediction of cardiovascular and renal risk among patients with apparent treatment-resistant hypertension in the United States using machine learning methods.

Apparent treatment-resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncont...

Quality assurance of late gadolinium enhancement cardiac magnetic resonance images: a deep learning classifier for confidence in the presence or absence of abnormality with potential to prompt real-time image optimization.

BACKGROUND: Late gadolinium enhancement (LGE) of the myocardium has significant diagnostic and progn...

Recurrent neural network-based simultaneous cardiac T1, T2, and T1ρ mapping.

The purpose of the current study was to explore the feasibility of training a deep neural network to...