Strokes

Prediction of NIHSS Scores and Acute Ischemic Stroke Severity Using a Cross-attention Vision Transformer Model with Multimodal MRI.

RATIONALE AND OBJECTIVES: This study aimed to develop and evaluate models for classifying the severity of neurological impairment in acute ischemic stroke (AIS) patients using multimodal MRI data.

Machine learning application for bleeding risk prediction in patients with atrial fibrillation treated with oral anticoagulation.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with a significantly increased risk of systemic thromboembolism and stroke. Anticoagulation therapy, particularly with Direct Oral Anticoagulants, has become the standard for stroke prevention but comes at the cost of an increased bleeding risk. With the introduction of effective alternatives to anticoagulation, such as percutan...

Precision-Optimised Post-Stroke Prognoses.

BACKGROUND: Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by t...

Predicting Stroke-Associated Pneumonia in Acute Ischemic Stroke: A Machine Learning Model Development and Validation Study with CBC-Derived Inflammatory Indices.

PURPOSE: Stroke-associated pneumonia (SAP), a critical complication of ischemic stroke, significantly worsens outcomes. Our aim was to identify SAP ri...

Gene as a Molecular Target Potentially Shared by Idiopathic Pulmonary Fibrosis and Pulmonary Hypertension: Drug Repurposing Prospect with Aminoglutethimide.

Idiopathic pulmonary fibrosis (IPF) and pulmonary hypertension (PH) are two chronic conditions that can coexist occasionally, resulting in high morbid...

Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employing machine learning methods.

BACKGROUND: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide. Intravenous thrombolysis (IVT) improves recovery, bu...

Comparison of lesion segmentation performance in diffusion-weighted imaging and apparent diffusion coefficient images of stroke by artificial neural networks.

Stroke is the second leading cause of death, accounting for 11% of deaths worldwide. Comparing diffusion-weighted imaging (DWI) and apparent diffusion...

Research on ischemic stroke risk assessment based on CTA radiomics and machine learning.

BACKGROUND: The study explores the value of a model constructed by integrating CTA-based carotid plaque radiomic features, clinical risk factors, and ...

Machine-learning model for predicting left atrial thrombus in patients with paroxysmal atrial fibrillation.

OBJECTIVE: Left atrial thrombus (LAT) poses a significant risk for stroke and other thromboembolic complication in patients with atrial fibrillation (...

Stroke Sensitivity Calculation in Medical Emergency Calls and Factors Associated With Stroke Suspicion: A Retrospective Registry-Based Study.

STUDY OBJECTIVE: Sensitivity for stroke detection in emergency medical communication centers (EMCCs) varies widely. Few studies offer detailed insight...

The Effects of Robot-Assisted Therapy on Upper Extremity Functions and Functional Independence in Geriatric Individuals With Chronic Stroke: A Randomized Controlled Study.

AimStroke often leads to impaired motor functions, particularly in upper extremities, making functional recovery essential for quality of life and ind...

Prospective Evaluation of Artificial Intelligence Imaging Support Software for Acute Ischemic Stroke in the Mayo Clinic Telestroke Network.

OBJECTIVE: To explore the real-world impact of artificial intelligence-driven decision support imaging software for patients with acute ischemic strok...

Benchmarking machine learning models in lesion-symptom mapping for predicting language outcomes in stroke survivors.

Several decades of research have investigated the neural connections between stroke-induced brain damage and language difficulties. Typically, lesion-...

Harnessing AI for aphasia: a case report on ChatGPT's role in supporting written expression.

INTRODUCTION: Aphasia is associated with impairments in written language, including difficulty with sentence formulation, word finding, and editing. W...

The use of imaging in the diagnosis and treatment of thromboembolic pulmonary hypertension.

Chronic thromboembolic pulmonary hypertension (CTEPH) is a potentially life-threatening condition, classified as group 4 pulmonary hypertension (PH), ...

Bioinformatics prediction of function of T-cell exhaustion related genes in ischemic stroke.

Ischemic stroke (IS) is a multifactorial disease caused by the interaction of a variety of environmental and genetic factors, which can lead to severe...

Gut microbiota, metabolites, and pulmonary hypertension: Mutual regulation and potential therapies.

Pulmonary hypertension is a progressive condition characterized by increased pulmonary vascular pressure and resistance, ultimately leading to right h...

Association between the stress hyperglycemia ratio and all-cause mortality in patients with hemorrhagic stroke: a retrospective analysis based on MIMIC-IV database.

BACKGROUND: Research on the associations between the stress hyperglycemia ratio (SHR) and adverse outcomes in patients with hemorrhagic stroke is limi...

Blood pressure monitoring is key in aortic dissection.

Blood pressure (BP) control is essential for both the prevention and long-term management of aortic dissection. While office BP monitoring remains the...

Multiscale Organization of Neural Networks in a 3D Bioprinted Matrix.

The efficient establishment of in vitro neural models that accurately mimic the structural and functional connectivity of neural networks is critical ...