Strokes

High-throughput DeepPRM-Stellar proteomics coupled with machine learning enables precise quantification of atherosclerosis-stroke progression biomarkers and risk prediction.

Predicting the progression of asymptomatic large-artery atherosclerosis (LAA) to acute ischemic stro...

Towards Automated Eye Movement Characterization for Stroke Patients Using Synthetic Video Data and Machine Learning.

Stroke is a critical medical emergency that can cause permanent disability or death. Rapid identific...

Single-cell omics: moving towards a new era in ischemic stroke research.

Ischemic stroke (IS) is a highly complex and heterogeneous disease involving multiple pathophysiolog...

Developing an Explainable Prognostic Model for Acute Ischemic Stroke: Combining Clinical and Inflammatory Biomarkers With Machine Learning.

BACKGROUND: Predicting the prognosis of patients with acute cerebral infarction (ACI) is crucial for...

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 imagi...

Associations of the Hs-CRP/HDL-C ratio with stroke among US adults: Evidence from NHANES 2015-2018.

BACKGROUND: The high-sensitivity C-reactive protein (Hs-CRP)-to-high-density lipoprotein cholesterol...

Genetic Risk Scores in Stroke Research and Care.

Stroke remains a leading cause of death and disability worldwide. While well-established risk factor...

Distinct Disconnection Patterns Explain Task-Specific Motor Impairment and Outcome After Stroke.

BACKGROUND: Stroke is increasingly understood as a network disorder with symptoms often arising from...

Anticoagulation colloidal microrobots based on heparin-mimicking polymers.

Coagulation within blood vessels is a major cause of cardiovascular disease and global mortality, hi...

Artificial Intelligence to Improve Blood Pressure Control: A State-of-the-Art Review.

Hypertension remains a major global health challenge, contributing to significant morbidity and mort...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for ...

Bleeding risk assessment tools in patients with atrial fibrillation taking anticoagulants: a comparative review and clinical implications.

INTRODUCTION: Bleeding risk assessment plays a critical role in the anticoagulation management for a...

ABCD: A Simulation Method for Accelerating Conversational Agents With Applications in Aphasia Therapy.

PURPOSE: Development of aphasia therapies is limited by clinician shortages, patient recruitment cha...

Prediction of tissue and clinical thrombectomy outcome in acute ischaemic stroke using deep learning.

The advent of endovascular thrombectomy has significantly improved outcomes for stroke patients with...

Integrating AI/ML and multi-omics approaches to investigate the role of TNFRSF10A/TRAILR1 and its potential targets in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a five-yea...

Unravelling the brain resilience following stroke: From injury to rewiring of the brain through pathway activation, drug targets, and therapeutic interventions.

Synaptic plasticity is a neuron's intrinsic ability to make new connections throughout life. The mor...

Prediction of post stroke depression with machine learning: A national multicenter cohort study.

OBJECTIVE: Post-stroke depression (PSD) is a common psychiatric complication following stroke, with ...

Impact of spectrum bias on deep learning-based stroke MRI analysis.

PURPOSE: To evaluate spectrum bias in stroke MRI analysis by excluding cases with uncertain acute is...

An informed machine learning based environmental risk score for hypertension in European adults.

BACKGROUND: The exposome framework seeks to unravel the cumulated effects of environmental exposures...

Focus on juvenile hypertension, deep learning-qualified body composition for risk stratification, infective endocarditis, and mixed valvular heart disease.

Two-stage convolutional neural network for segmentation and detection of carotid web on CT angiography.

BACKGROUND: Carotid web (CaW) is a risk factor for ischemic stroke, mainly in young patients with st...