Strokes

Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

BACKGROUND: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated. By identifying patients at high risk of embolic stroke, health-care professionals can make more informed decisions regarding anticoagulation treatment to prevent stroke. The main aim of this study was to forecast the risk of AF both retrospectively and prospectively.

An Integrative Machine Learning Model for Predicting Early Safety Outcomes in Patients Undergoing Transcatheter Aortic Valve Implantation.

: Early safety outcomes following transcatheter aortic valve implantation (TAVI) for severe aortic stenosis are critical for patient prognosis. Accurate prediction of adverse events can enhance patient management and improve outcomes. : This study aimed to develop a machine learning model to predict early safety outcomes in patients with severe aortic stenosis undergoing TAVI. : We conducted a ret...

Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.

BACKGROUND: Sarcopenia is a common complication in patients with stroke, adversely affecting recovery and increasing mortality risk. However, no stand...

Intracranial stenosis prediction using a small set of risk factors in the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) refers to a narrowing of intracranial arteries due to plaque buildup on the inside of the vessel walls re...

Clinical efficacy of NIBS in enhancing neuroplasticity for stroke recovery.

BACKGROUND: For stroke patients, a therapeutic approach named Non-invasive brain stimulation (NIBS) was applied and it has gained attention. This NIBS...

Deep learning-based classification of diffusion-weighted imaging-fluid-attenuated inversion recovery mismatch.

The presence of a diffusion-weighted imaging (DWI)-fluid-attenuated inversion recovery (FLAIR) mismatch holds potential value in identifying candidate...

Application of Artificial Intelligence in Acute Ischemic Stroke: A Scoping Review.

Artificial intelligence (AI) is revolutionizing stroke care by enhancing diagnosis, treatment, and outcome prediction. This review examines 505 origin...

Machine-learning tool for classifying pulmonary hypertension via expert reader-provided CT features: An educational resource for non-dedicated radiologists.

PURPOSE: Pulmonary hypertension (PH) is a complex disease classified into five groups (I-V) by the European Society of Cardiology/European Respiratory...

Classification patterns identification of immunogenic cell death-related genes in heart failure based on deep learning.

Heart failure (HF) is a complex and prevalent condition, particularly in the elderly, presenting symptoms like chest tightness, shortness of breath, a...

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 in ischemic stroke patients undergoing endovascula...

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 transformer large language models (LLMs). They have been t...

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 anticoagulation, but optimal patient selection remains ...

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, in particular stroke, which is one of the leading c...

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 goes undiagnosed in part because of low suspicion an...

Hybrid deep learning based stroke detection using CT images with routing in an IoT environment.

Stroke remains a leading global health concern and early diagnosis and accurate identification of stroke lesions are essential for improving treatment...

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 gait recovery in patients with acute anterior circul...

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 stratification tools such as CHADS-VASc and QRISK3 are ...

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 careful anticoagulation management. With rising r...

Comparative efficacy of robot-assisted therapy associated with other different interventions on upper limb rehabilitation after stroke: A protocol for a network meta-analysis.

INTRODUCTION: Post-stroke movement disorders are common, especially upper limb dysfunction, which seriously affects the physical and mental health of ...

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 populations bearing a disproportionately high burden due to...