Cardiovascular

Congestive Heart Failure

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

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A bibliometric review of peripartum cardiomyopathy compared to other cardiomyopathies using artificial intelligence and machine learning.

As developments in artificial intelligence and machine learning become more widespread in healthcare...

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...

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

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

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...

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...

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...

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...

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...

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...

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...

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...

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 ...

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection.

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

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