Cardiovascular

Congestive Heart Failure

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

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Showing 232-252 of 3,374 articles
CiGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation.

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuou...

Integration of Cine-cardiac Magnetic Resonance Radiomics and Machine Learning for Differentiating Ischemic and Dilated Cardiomyopathy.

RATIONALE AND OBJECTIVES: This study aims to evaluate the capability of machine learning algorithms ...

Recognition of diabetic retinopathy and macular edema using deep learning.

Diabetic retinopathy (DR) and diabetic macular edema (DME) are both serious eye conditions associate...

A Case Report of Becker Muscular Dystrophy and Stroke Who Successfully Regained Mobility With Robot-Assisted Gait Training.

A 30-yr-old patient with Becker muscular dystrophy presented with stroke. Background issues of proxi...

Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions.

To address the challenge of meningioma grading, this study aims to investigate the potential value o...

Integrating decision modeling and machine learning to inform treatment stratification.

There is increasing interest in moving away from "one size fits all (OSFA)" approaches toward strati...

Applying Artificial Intelligence for Phenotyping of Inherited Arrhythmia Syndromes.

Inherited arrhythmia disorders account for a significant proportion of sudden cardiac death, particu...

W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT.

Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in...

Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho...

A new intelligent system based deep learning to detect DME and AMD in OCT images.

Optical Coherence Tomography (OCT) is widely recognized as the leading modality for assessing ocular...

Machine learning-driven diagnostic signature provides new insights in clinical management of hypertrophic cardiomyopathy.

AIMS: In an era of evolving diagnostic possibilities, existing diagnostic systems are not fully suff...

Radiomics-based detection of acute myocardial infarction on noncontrast enhanced midventricular short-axis cine CMR images.

Cardiac magnetic resonance cine images are primarily used to evaluate functional consequences, where...

OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods.

Optical coherence tomography (OCT) is a non-invasive imaging technique with extensive clinical appli...

Artificial Intelligence for Cardiovascular Care-Part 1: Advances: JACC Review Topic of the Week.

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements...

Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.

BACKGROUND: Left bundle branch block (LBBB) induced cardiomyopathy is an increasingly recognized dis...

DeepMesh: Mesh-Based Cardiac Motion Tracking Using Deep Learning.

3D motion estimation from cine cardiac magnetic resonance (CMR) images is important for the assessme...

An Explainable Artificial Intelligence Model to Predict Malignant Cerebral Edema after Acute Anterior Circulating Large-Hemisphere Infarction.

INTRODUCTION: Malignant cerebral edema (MCE) is a serious complication and the main cause of poor pr...

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