Congestive Heart Failure

Comprehensive electrocardiographic diagnosis based on deep learning.

Cardiovascular disease (CVD) is the leading cause of death worldwide, and coronary artery disease (CAD) is a major contributor. Early-stage CAD can progress if undiagnosed and left untreated, leading to myocardial infarction (MI) that may induce irreversible heart muscle damage, resulting in heart chamber remodeling and eventual congestive heart failure (CHF). Electrocardiography (ECG) signals can...

Expression of Cytokines and Chemokines as Predictors of Stroke Outcomes in Acute Ischemic Stroke.

Ischemic stroke remains one of the most debilitating diseases and is the fifth leading cause of death in the US. The ability to predict stroke outcomes within the acute period of stroke would be essential for care planning and rehabilitation. The Blood and Clot Thrombectomy Registry and Collaboration (BACTRAC; clinicaltrials.gov NCT03153683) study collects arterial blood immediately distal and pr...

Machine learning as new promising technique for selection of significant features in obese women with type 2 diabetes.

Background The global trend of obesity and diabetes is considerable. Recently, the early diagnosis and accurate prediction of type 2 diabetes mellitus...

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure.

The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the comb...

Effects of aged garlic extract on arterial elasticity in a placebo-controlled clinical trial using EndoPAT™ technology.

Cardiovascular diseases are the main cause of death in the industrialized world, with the main risk factors being elevated blood pressure and blood li...

Estimation of Arterial Blood Pressure Based on Artificial Intelligence Using Single Earlobe Photoplethysmography during Cardiopulmonary Resuscitation.

This study investigates the feasibility of estimation of blood pressure (BP) using a single earlobe photoplethysmography (Ear PPG) during cardiopulmon...

Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques.

Hypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention s...

Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage.

Background and Purpose- Volumes of hemorrhage and perihematomal edema (PHE) are well-established biomarkers of primary and secondary injury, respectiv...

AOCT-NET: a convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images.

Since introducing optical coherence tomography (OCT) technology for 2D eye imaging, it has become one of the most important and widely used imaging mo...

Three-dimensional Deep Convolutional Neural Networks for Automated Myocardial Scar Quantification in Hypertrophic Cardiomyopathy: A Multicenter Multivendor Study.

Background Cardiac MRI late gadolinium enhancement (LGE) scar volume is an important marker for outcome prediction in patients with hypertrophic cardi...

Assessment of ventricular tachyarrhythmia in patients with hypertrophic cardiomyopathy with machine learning-based texture analysis of late gadolinium enhancement cardiac MRI.

OBJECTIVE: To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic...

A predictive analytics framework for identifying patients at risk of developing multiple medical complications caused by chronic diseases.

Chronic diseases often cause several medical complications. This paper aims to predict multiple complications among patients with a chronic disease. T...

Automated label-free detection of injured neuron with deep learning by two-photon microscopy.

Stroke is a significant cause of morbidity and long-term disability globally. Detection of injured neuron is a prerequisite for defining the degree of...

Artificial Intelligence Meets Chinese Medicine.

As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention. The sta...

Fixation of intracapsular fracture of the femoral neck using combined peripheral nerve blocks and transthoracic echocardiography in a patient with severe obstructive hypertrophic cardiomyopathy: a case report.

BACKGROUND: Hypertrophic obstructive cardiomyopathy (HOCM) is a type of hypertrophic cardiomyopathy associated with left ventricular outflow tract ste...

A speckle-tracking strain-based artificial neural network model to differentiate cardiomyopathy type.

In heart failure, invasive angiography is often employed to differentiate ischaemic from non-ischaemic cardiomyopathy. We aim to examine the predicti...

Intelligent Imaging: Radiomics and Artificial Neural Networks in Heart Failure.

BACKGROUND: Our previous work with iodine meta-iodobenzylguanidine (I-mIBG) radionuclide imaging among patients with cardiomyopathy reported limitatio...

Improving the clinical understanding of hypertrophic cardiomyopathy by combining patient data, machine learning and computer simulations: A case study.

Most patients with hypertrophic cardiomyopathy (HCM), the most common genetic cardiac disease, remain asymptomatic, but others may suffer from sudden ...

Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology.

To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy a...

Machine Learning to Predict In-Hospital Morbidity and Mortality after Traumatic Brain Injury.

Recently, successful predictions using machine learning (ML) algorithms have been reported in various fields. However, in traumatic brain injury (TBI)...