Cardiovascular

Congestive Heart Failure

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

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Showing 1408-1428 of 4,489 articles
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 ...

Jul 2025 40024027
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...

Precision medicine applications in dilated cardiomyopathy: Advancing personalized care.

Dilated cardiomyopathy (DCM) is a prevalent cardiac disorder affecting 1 in 250-500 individuals, cha...

Jul 2025 40381754
Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transi...

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Jul 2025 40120825
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