Cardiovascular

Congestive Heart Failure

Latest AI and machine learning research in congestive heart failure for healthcare professionals.

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Deep learning-based automated left ventricular ejection fraction assessment using 2-D echocardiography.

Deep learning (DL) has been applied for automatic left ventricle (LV) ejection fraction (EF) measure...

Semi-Automated Determination of Heavy Metals in Autopsy Tissue Using Robot-Assisted Sample Preparation and ICP-MS.

The endoprosthetic care of hip and knee joints introduces multiple materials into the human body. Me...

Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos.

Zebrafish is a powerful and widely-used model system for a host of biological investigations, includ...

Artificial Intelligence-Augmented Electrocardiogram Detection of Left Ventricular Systolic Dysfunction in the General Population.

OBJECTIVE: To validate an artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm for...

Enhanced Diagnosis of Pneumothorax with an Improved Real-Time Augmentation for Imbalanced Chest X-rays Data Based on DCNN.

Pneumothorax is a common pulmonary disease that can lead to dyspnea and can be life-threatening. X-r...

Blood Biomarkers Predict Cardiac Workload Using Machine Learning.

INTRODUCTION: Rate pressure product (the product of heart rate and systolic blood pressure) is a mea...

A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation.

OBJECTIVE: At present, there is no consensus on the best strategy for interpreting the cardiopulmona...

Tinnitus Frequency is Higher in Patients with Chronic Heart Failure with Reduced Ejection Fraction and is Closely Related to NT-proBNP Level.

There is not enough information about tinnitus and related parameters in patients with heart failure...

Health improvement framework for actionable treatment planning using a surrogate Bayesian model.

Clinical decision-making regarding treatments based on personal characteristics leads to effective h...

Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction.

OBJECTIVES: The authors explored a deep neural network (DeepNN) model that integrates multidimension...

Machine learning based differentiation of glioblastoma from brain metastasis using MRI derived radiomics.

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial me...

Prediction of premature all-cause mortality in patients receiving peritoneal dialysis using modified artificial neural networks.

Premature all-cause mortality is high in patients receiving peritoneal dialysis (PD). The accurate a...

A machine learning model for identifying patients at risk for wild-type transthyretin amyloid cardiomyopathy.

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable...

NATURAL LANGUAGE PROCESSING BASED MACHINE LEARNING MODEL USING CARDIAC MRI REPORTS TO IDENTIFY HYPERTROPHIC CARDIOMYOPATHY PATIENTS.

Hypertrophic Cardiomyopathy (HCM) is the most common genetic heart disease in the US and is known to...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allerge...

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