Cardiovascular

Congestive Heart Failure

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

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Showing 778-798 of 3,389 articles
Multi-Omics Integration With Machine Learning Identified Early Diabetic Retinopathy, Diabetic Macula Edema and Anti-VEGF Treatment Response.

PURPOSE: Identify optimal metabolic features and pathways across diabetic retinopathy (DR) stages, d...

Shaping the future of heart health.

For World Heart Day on September 24, 2024, the World Heart Federation urges nations to endorse natio...

Towards consistency in pediatric brain tumor measurements: Challenges, solutions, and the role of artificial intelligence-based segmentation.

MR imaging is central to the assessment of tumor burden and changes over time in neuro-oncology. Sev...

Explainable machine learning prediction of edema adverse events in patients treated with tepotinib.

Tepotinib is approved for the treatment of patients with non-small-cell lung cancer harboring MET ex...

Anatomy-specific Progression Classification in Chest Radiographs via Weakly Supervised Learning.

Purpose To develop a machine learning approach for classifying disease progression in chest radiogra...

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of...

Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy.

Reducing electrophysiological (EP) signal noise is essential for diagnosis, mapping, and ablation, y...

Minimally invasive monitor of cardiac output based on the machine-learning analysis of the pulse contour of the peripheral arterial pressure.

In the hemodynamic management of anesthetized patients during surgical operation, minimally invasive...

Deep Learning for identifying systolic complexes in SCG traces: a cross-dataset analysis.

The seismocardiographic signal is a promising alternative to the traditional ECG in the analysis of ...

Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques.

Pancreatitis is a major public health issue world-wide; studies show an increase in the number of pe...

Unlocking Hidden Risks: Harnessing Artificial Intelligence (AI) to Detect Subclinical Conditions from an Electrocardiogram (ECG).

Recent artificial intelligence (AI) advancements in cardiovascular medicine offer potential enhancem...

Through the Looking Glass Darkly: How May AI Models Influence Future Underwriting?

Applications of Artificial Intelligence (AI) deep-learning models to screening for clinical conditio...

[Prediction of risk of in-hospital death in patients with chronic heart failure complicated by lung infections using interpretable machine learning].

OBJECTIVE: To predict the risk of in-hospital death in patients with chronic heart failure (CHF) com...

Dissecting unique and common variance across body and brain health indicators using age prediction.

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological ...

Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.

BACKGROUND: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in rig...

Visualization of Optic Nerve Structural Patterns in Papilledema Using Deep Learning Variational Autoencoders.

PURPOSE: To visualize and quantify structural patterns of optic nerve edema encountered in papillede...

Fine grained automatic left ventricle segmentation via ROI based Tri-Convolutional neural networks.

BACKGROUND: The left ventricle segmentation (LVS) is crucial to the assessment of cardiac function. ...

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