Cardiovascular

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

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Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.

BACKGROUND: Deep learning using clinical and imaging data may improve pre-treatment prognostication ...

An arrhythmia classification using a deep learning and optimisation-based methodology.

The work proposes a methodology for five different classes of ECG signals. The methodology utilises ...

Active learning and margin strategies for arrhythmia classification in implantable devices.

BACKGROUND AND OBJECTIVES: The massive storage of cardiac arrhythmic episodes from Implantable Cardi...

Large Language Models-Supported Thrombectomy Decision-Making in Acute Ischemic Stroke Based on Radiology Reports: Feasibility Qualitative Study.

BACKGROUND: The latest advancement of artificial intelligence (AI) is generative pretrained transfor...

Climate change and cardiovascular risk.

PURPOSE OF REVIEW: This review explores the complex relationship between climate change and cardiova...

Causal Machine Learning for Left Atrial Appendage Occlusion in Patients With Atrial Fibrillation.

BACKGROUND: Transcatheter left atrial appendage occlusion (LAAO) is an alternative to lifelong antic...

Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.

PURPOSE: To determine how automation bias (inclination of humans to overly trust-automated decision-...

Spherical lesion formation in HIFU using robotic assistance for controlled focal point manipulation.

We propose a robot-assisted method to generate spherical thermal lesions by high-intensity focused u...

Deep CNN-based detection of cardiac rhythm disorders using PPG signals from wearable devices.

Cardiac rhythm disorders can manifest in various ways, such as the heart rate being too fast (tachyc...

Predicting Atrial Fibrillation Relapse Using Bayesian Networks: Explainable AI Approach.

BACKGROUND: Atrial fibrillation (AF) is a prevalent arrhythmia associated with significant morbidity...

Deep attention model for arrhythmia signal classification based on multi-objective crayfish optimization algorithmic variational mode decomposition.

The detection and classification of arrhythmia play a vital role in the diagnosis and management of ...

AI Accelerator With Ultralightweight Time-Period CNN-Based Model for Arrhythmia Classification.

This work proposes a classification system for arrhythmias, aiming to enhance the efficiency of the ...

Association Between Aortic Imaging Features and Impaired Glucose Metabolism: A Deep Learning Population Phenotyping Approach.

RATIONALE AND OBJECTIVES: Type 2 diabetes is a known risk factor for vascular disease with an impact...

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