Cardiovascular

Latest AI and machine learning research in cardiovascular for healthcare professionals.

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Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.

OBJECTIVE: This study aims to assess the feasibility of "double-low," low radiation dosage and low c...

Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy.

Sudden cardiac death from ventricular arrhythmias is a leading cause of mortality worldwide. Arrhyth...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracrani...

Generative artificial intelligence for fundus fluorescein angiography interpretation and human expert evaluation.

Fundus fluorescein angiography (FFA) is the gold standard for diagnosing chorioretinal diseases, but...

Key factors in predictive analysis of cardiovascular risks in public health.

This research emphasizes the role of analytics in evaluating the risk of disease (CVD) focusing on t...

Gene therapy and genome editing for lipoprotein disorders.

Genetic factors play a critical role in the development of lipoprotein disorders, which significantl...

Exploring predictive models for intradialytic hypotension risk in maintenance hemodialysis patients: A systematic review.

BACKGROUND: This systematic review evaluates existing risk prediction models for intradialytic hypot...

Associations of street-view greenspace exposure with cardiovascular health (Life's Essential 8) among women in midlife.

BACKGROUND: Many women experience suboptimal cardiovascular health (CVH) during midlife. Greenspace ...

Transforming heart transplantation care with multi-omics insights.

Heart transplantation (HTx) remains the definitive treatment for patients with end-stage heart disea...

Multisensory BCI promotes motor recovery via high-order network-mediated interhemispheric integration in chronic stroke.

BACKGROUND: Chronic stroke patients often experience persistent motor impairments, and current rehab...

An explainable machine learning model for early warning of hypertensive and hypotensive anomalies in maintenance hemodialysis patients.

BACKGROUND: Intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN) are major complic...

Lactylation associated biomarkers and immune infiltration in aortic dissection.

Protein lactylation, a novel post-translational modification (PTM), has emerged as a critical factor...

A machine learning-based framework for predicting metabolic syndrome using serum liver function tests and high-sensitivity C-reactive protein.

Metabolic Syndrome (MetS) comprises a clustering of conditions that significantly increase the risk ...

Metabolomic biomarkers could be molecular clocks in timing stroke onset.

The preferred treatment for acute ischaemic stroke (AIS) is intravenous thrombolysis (IVT) administe...

Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.

Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can sign...

Overcoming data scarcity in life-threatening arrhythmia detection through transfer learning.

BACKGROUND: Life-threatening arrhythmias (LTAs) are a leading cause of death worldwide. Enhancing LT...

DeepECG-Net: a hybrid transformer-based deep learning model for real-time ECG anomaly detection.

Real-time Electrocardiogram (ECG) anomaly detection is critical for accurate diagnosis and timely in...

Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble.

A machine learning model was developed and validated to predict postoperative complications in patie...

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