Cardiovascular

Congestive Heart Failure

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

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Showing 1072-1092 of 3,596 articles
Multimodal Machine Learning Reveals the Genomic and Proteomic Architecture of Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) affects over 30 million people and lacks dise...

Addressing data annotation scarcity in Brain Tumor Segmentation on 3D MRI scan Using a Semi-Supervised Teacher-Student Framework

Accurate brain tumor segmentation from MRI is limited by expensive annotations and data heterogeneit...

Orientation-Robust Latent Motion Trajectory Learning for Annotation-free Cardiac Phase Detection in Fetal Echocardiography

Fetal echocardiography is essential for detecting congenital heart disease (CHD), facilitating pregn...

ESUS-AI:a machine learning framework to estimate the most likely embolic source in embolic stroke of undetermined source

Background and Purpose Embolic stroke of undetermined source (ESUS) emains a major diagnostic challe...

Personalised approach to hypertension treatment: Rationale and design of the HYPERMARKER randomised trial

Background and Objective: Blood pressure treatment response is variable in individual patients, and ...

Machine Learning Ensemble Reveals Distinct Molecular Pathways of Retinal Damage in Spaceflown Mice

Spaceflight-associated neuro-ocular syndrome (SANS) threatens astronaut health during long-duration ...

Deep Learning Decodes Latent ECG Signatures of Stress Cardiomyopathy

Background Stress cardiomyopathy (SCM) shares features with acute myocardial infarction (AMI) which ...

Gene-exposure interactions regulate cytokine-mediated chronic inflammation and cardiac remodeling

Background: Chronic inflammation predicts adverse cardiovascular outcomes, but mechanisms linking sy...

Artificial Intelligence-Enabled Echocardiographic Assessment of Right Ventricular Function

Background: Right ventricular (RV) function is an important predictor of morbidity and mortality in ...

Using Artificial Intelligence to Assess Treatment-Effect Heterogeneity in Pragmatic Cardiovascular Trials: Insights from TRANSFORM-HF

Background and Aims: Pragmatic clinical trials are designed to assess interventions in real-world se...

Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram

BackgroundAtrial cardiomyopathy (AtCM) is both a cause and a consequence of atrial fibrillation and ...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for ...

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Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation

Synthetic images are an option for augmenting limited medical imaging datasets to improve the perf...

CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR

Myocardial infarction (MI) is a leading cause of death worldwide. Late gadolinium enhancement (LGE...

SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations

The emergence of diffusion models has significantly advanced generative AI, improving the quality,...

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound

Mitral regurgitation is one of the most prevalent cardiac disorders. Four-dimensional (4D) ultraso...

Physics-Informed Neural ODEs for Temporal Dynamics Modeling in Cardiac T1 Mapping

Spin-lattice relaxation time ($T_1$) is an important biomarker in cardiac parametric mapping for c...

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