Congestive Heart Failure

Toward Replacing Late Gadolinium Enhancement With Artificial Intelligence Virtual Native Enhancement for Gadolinium-Free Cardiovascular Magnetic Resonance Tissue Characterization in Hypertrophic Cardiomyopathy.

BACKGROUND: Late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) imaging is the gold standard for noninvasive myocardial tissue characterization but requires intravenous contrast agent administration. It is highly desired to develop a contrast agent-free technology to replace LGE for faster and cheaper CMR scans.

Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging.

Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective analysis of 100 healthy subjects undergoing cardiac MRI from 2012 to 2018, with 10 men and women in the following age groups: 18-30, 31-40, 41-50, 51-60,...

Combined deep CNN-LSTM network-based multitasking learning architecture for noninvasive continuous blood pressure estimation using difference in ECG-PPG features.

The pulse arrival time (PAT), the difference between the R-peak time of electrocardiogram (ECG) signal and the systolic peak of photoplethysmography (...

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) measurement, but the diagnostic performance was rarely ev...

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. Metal containing implant surfaces release degradatio...

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, including cardiovascular studies and genetic screening. ...

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 the detection of preclinical left ventricular sys...

A computational pipeline for data augmentation towards the improvement of disease classification and risk stratification models: A case study in two clinical domains.

Virtual population generation is an emerging field in data science with numerous applications in healthcare towards the augmentation of clinical resea...

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-ray examination is the main means to diagnose this ...

Blood Biomarkers Predict Cardiac Workload Using Machine Learning.

INTRODUCTION: Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure pr...

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 cardiopulmonary exercise test's (CPET) results. This study is a...

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 with reduced ejection fraction (HFrEF). In our st...

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

Clinical decision-making regarding treatments based on personal characteristics leads to effective health improvements. Machine learning (ML) has been...

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

OBJECTIVES: The authors explored a deep neural network (DeepNN) model that integrates multidimensional echocardiographic data to identify distinct pat...

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 metastatic disease (IMD). However, the effect of dif...

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 and early prediction of mortality is critical and d...

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 with a transthyretin stabilizer. It is therefore ...

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 cause sudden death (SCD) in young adults. While s...

Machine Learning Can Predict Anti-VEGF Treatment Demand in a Treat-and-Extend Regimen for Patients with Neovascular AMD, DME, and RVO Associated Macular Edema.

PURPOSE: To assess the potential of machine learning to predict low and high treatment demand in real life in patients with neovascular age-related ma...

Conjunctival Provocation Test With .

Conjunctival provocation test (CPT) is used to demonstrate clinical relevance to a specific allergen. (Bt) is a prevalent allergen in tropical regio...