Cardiovascular

Strokes

Latest AI and machine learning research in strokes 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 ...

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...

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...

Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.

The relevance of the study is due to the growing number of diseases of the cerebrovascular system, i...

Development and Evaluation of a Deep Learning-Based Pulmonary Hypertension Screening Algorithm Using a Digital Stethoscope.

BACKGROUND: Despite the poor outcomes related to the presence of pulmonary hypertension, it often go...

Machine learning techniques for independent gait recovery prediction in acute anterior circulation ischemic stroke.

OBJECTIVE: This study aimed to develop and validate a machine learning-based predictive model for ga...

Machine learning to predict stroke risk from routine hospital data: A systematic review.

PURPOSE: Stroke remains a leading cause of morbidity and mortality. Despite this, current risk strat...

Predictive models of clinical outcome of endovascular treatment for anterior circulation stroke using machine learning.

BACKGROUND AND PURPOSE: Mechanical Thrombectomy (MT) has recently become the standard of care for an...

Optimizing warfarin dosing in diabetic patients through BERT model and machine learning techniques.

This study highlights the importance of evaluating warfarin dosing in diabetic patients, who require...

Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa.

BACKGROUND: Stroke remains a leading cause of death and disability worldwide, with African populatio...

Identification of Novel Biomarkers for Ischemic Stroke Through Integrated Bioinformatics Analysis and Machine Learning.

Ischemic stroke leads to permanent damage to the affected brain tissue, with strict time constraints...

Ledged Beam Walking Test Automatic Tracker: Artificial intelligence-based functional evaluation in a stroke model.

The quantitative evaluation of motor function in experimental stroke models is essential for the pre...

A comprehensive analysis of stroke risk factors and development of a predictive model using machine learning approaches.

Stroke is a leading cause of death and disability globally, particularly in China. Identifying risk ...

Clinical validation of an individualized auto-adaptative serious game for combined cognitive and upper limb motor robotic rehabilitation after stroke.

BACKGROUND: Intensive rehabilitation through challenging and individualized tasks are recommended to...

An interpretable hybrid machine learning approach for predicting three-month unfavorable outcomes in patients with acute ischemic stroke.

BACKGROUND: Acute ischemic stroke (AIS) is a clinical disorder caused by nontraumatic cerebrovascula...

A novel hybrid ViT-LSTM model with explainable AI for brain stroke detection and classification in CT images: A case study of Rajshahi region.

Computed tomography (CT) scans play a key role in the diagnosis of stroke, a leading cause of morbid...

Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.

Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke...

Lean body mass and stroke volume, a sex issue.

INTRODUCTION: Large vessel occlusions (LVO) account for over 60% of stroke-related mortality and dis...

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