Cardiovascular

Congestive Heart Failure

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

3,592 articles
Stay Ahead - Weekly Congestive Heart Failure research updates
Subscribe
Browse Categories
Showing 694-714 of 3,592 articles
Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management.

This study was aimed at exploring the new management mode of medical information processing and emer...

Feb 2022 35154360
Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning.

Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are...

Feb 2022 35185594
Using deep learning models to analyze the cerebral edema complication caused by radiotherapy in patients with intracranial tumor.

Using deep learning models to analyze patients with intracranial tumors, to study the image segmenta...

Jan 2022 35091636
Development, validation, and application of a machine learning model to estimate salt consumption in 54 countries.

Global targets to reduce salt intake have been proposed, but their monitoring is challenged by the l...

Jan 2022 34984979
Application of ensemble machine learning algorithms on lifestyle factors and wearables for cardiovascular risk prediction.

This study looked at novel data sources for cardiovascular risk prediction including detailed lifest...

Jan 2022 35058500
Soft Transducer for Patient's Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection.

This work addresses the design, development and implementation of a 4.0-based wearable soft transduc...

Jan 2022 35062496
Deep Learning to Detect OCT-derived Diabetic Macular Edema from Color Retinal Photographs: A Multicenter Validation Study.

PURPOSE: To validate the generalizability of a deep learning system (DLS) that detects diabetic macu...

Jan 2022 34999015
Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT.

Automatic cardiac chamber and left ventricular (LV) myocardium segmentation over the cardiac cycle s...

Dec 2021 35026571
Explainable Machine Learning for Atrial Fibrillation in the General Population Using a Generalized Additive Model - A Cross-Sectional Study.

Atrial fibrillation (AF) is the most common arrhythmia and is associated with increased thromboembo...

Dec 2021 35178483
Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 p...

Dec 2021 34973585
Use of machine learning to classify high-risk variants of uncertain significance in lamin A/C cardiac disease.

BACKGROUND: Variation in lamin A/C results in a spectrum of clinical disease, including arrhythmias ...

Dec 2021 34958940
Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

Today, accurate and automated abnormality diagnosis and identification have become of paramount impo...

Dec 2021 35009666
Left ventricular systolic dysfunction predicted by artificial intelligence using the electrocardiogram in Chagas disease patients-The SaMi-Trop cohort.

BACKGROUND: Left ventricular systolic dysfunction (LVSD) in Chagas disease (ChD) is relatively commo...

Dec 2021 34871321
Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery.

OBJECTIVES: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM...

Dec 2021 34946895
Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study.

BACKGROUND: Echocardiography is the diagnostic modality for assessing cardiac systolic and diastolic...

Dec 2021 34863649
A machine learning framework for the evaluation of myocardial rotation in patients with noncompaction cardiomyopathy.

AIMS: Noncompaction cardiomyopathy (NCC) is considered a genetic cardiomyopathy with unknown pathoph...

Nov 2021 34843536
A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symp...

Nov 2021 34873413
Browse Categories