Cardiovascular

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

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Integrated multi-omics analysis describes immune profiles in ischemic heart failure and identifies PTN as a novel biomarker.

INTRODUCTION: Heart failure is a leading global cause of mortality, with ischemic heart failure (IHF...

Subtyping strokes using blood-based protein biomarkers: A high-throughput proteomics and machine learning approach.

BACKGROUND: Rapid diagnosis of stroke and its subtypes is critical in early stages. We aimed to disc...

Convolutional neural networks for automatic MR classification of myocardial iron overload in thalassemia major patients.

OBJECTIVES: To develop a deep-learning model for supervised classification of myocardial iron overlo...

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges.

Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors ...

End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

PURPOSE: To develop an end-to-end DL model for automated classification of affected territory in DWI...

An exploration into the diagnostic capabilities of microRNAs for myocardial infarction using machine learning.

BACKGROUND: MicroRNAs (miRNAs) have shown potential as diagnostic biomarkers for myocardial infarcti...

Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.

To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevan...

Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction.

OBJECTIVES: To investigate the impact of systemic inflammatory response syndrome (SIRS) on 30-day mo...

A systematic review on the impact of artificial intelligence on electrocardiograms in cardiology.

BACKGROUND: Artificial intelligence (AI) has revolutionized numerous industries, enhancing efficienc...

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes.

The development of computational models for the prediction of cardiac cellular dynamics remains a ch...

Deep learning for cardiac imaging: focus on myocardial diseases, a narrative review.

The integration of computational technologies into cardiology has significantly advanced the diagnos...

Automated Segmentation of MRI White Matter Hyperintensities in 8421 Patients with Acute Ischemic Stroke.

BACKGROUND AND PURPOSE: To date, only a few small studies have attempted deep learning-based automat...

An Energy-Efficient ECG Processor With Ultra-Low-Parameter Multistage Neural Network and Optimized Power-of-Two Quantization.

This work presents an energy-efficient ECG processor designed for Cardiac Arrhythmia Classification....

Clinical feasibility study of transcatheter edge-to-edge mitral valve repair in dogs with the canine V-Clamp device.

OBJECTIVE: To determine procedural feasibility, safety, and short-term efficacy in dogs with severe ...

SMOC2, OGN, FCN3, and SERPINA3 could be biomarkers for the evaluation of acute decompensated heart failure caused by venous congestion.

BACKGROUND: Venous congestion (VC) sets in weeks before visible clinical decompensation, progressive...

Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological diso...

Revisiting the Endoscopic Third Ventriculostomy Success Score using machine learning: can we do better?

OBJECTIVE: The Endoscopic Third Ventriculostomy Success Score (ETVSS) is a useful decision-making he...

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