Strokes

Artificial intelligence (AI)-powered diagnostic support for stroke via Telegram bot: preliminary findings.

BACKGROUND: Annually, stroke affects over 15 million people globally. Early intervention is critical...

Retinal Microvascular Signatures as Early Predictors of Cardiovascular Risk: Integrating Pathophysiology, Molecular Insights, and Artificial Intelligence.

Cardiovascular diseases (CVDs) continue to be a significant public health burden and public health e...

Prognostic value of AI-derived epicardial adipose tissue beyond coronary artery calcium on chest CT in an asymptomatic screening population.

BACKGROUND: The incremental prognostic value of epicardial adipose tissue (EAT) quantified on nongat...

Generating Synthetic MR Perfusion Maps From DWI and FLAIR in Acute Ischemic Stroke: Development and External Validation of a Deep Learning Model.

BACKGROUND: Magnetic resonance imaging (MRI) is critical for acute stroke triage, but it is time-con...

Diagnostic performance of commercial AI systems versus participating radiologists for pulmonary nodule detection in routine clinical practice.

PURPOSE: To evaluate the diagnostic performance of two commercial artificial intelligence (AI) syste...

A Framework with Transformer-Based Model for Cerebrovascular Stenosis Detection in Magnetic Resonance Angiography.

Accurate identification of cerebrovascular stenosis is essential for early stroke prevention and eff...

Towards trustworthy brain stroke diagnosis using a lightweight explainable deep learning framework for CT imaging.

Brain stroke occurs due to blockage or rupture in the cerebral blood supply and represents a critica...

Multiomics reveals fatty acid metabolism and immune remodeling in retinal artery occlusion.

Retinal artery occlusion (RAO), an acute ocular ischemic stroke, is critically linked to cardiovascu...

Applying machine learning to pharmacovigilance data: A proof-of-concept study.

AIM: Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using...

Who may benefit from robot-assisted gait training with an exoskeleton in subacute stroke patients? A prespecified analysis.

OBJECTIVE: To identify factors associated with the achievement of independent gait after the robot-a...

Enhancing Non-Contrast CT Interpretation for Acute Anterior Circulation Stroke: A Deep Learning Approach for Grayscale Image Transformation.

BACKGROUND AND PURPOSE: To develop a two-stage framework that combines deep learning-based super-res...

Validation of latent health behaviour subtypes in young and middle-aged adults with hypertension: a machine learning-augmented LCA approach with external cohort replication.

AIMS: To validate behavioural subtypes among young and middle-aged hypertensive patients using laten...

nnU-Net-based CT segmentation of perigastric varices in sinistral portal hypertension: a multicenter study.

PURPOSE: To develop a deep learning model based on nnU-Net for automated segmentation of all perigas...

Machine learning to identify novel bleeding and residual thromboembolic risks in patients on anticoagulation.

Balancing thromboembolic prevention against bleeding risk remains a key challenge during oral antico...

Comment on "Advancing Personalised Care in Atrial Fibrillation and Stroke: The Potential Impact of AI from Prevention to Rehabilitation".

Interpretable Machine Learning in Heavy Metal-Associated Cardiovascular and Metabolic Disease: A Review of Current Methodologies, Toxicological Insights, and Clinical Implications.

Heavy metals, including lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg), are pervasive envir...

Machine learning versus deep learning for screening ischemic stroke among asymptomatic population.

Ischemic stroke puts great health burden in public. However, the diagnosis is based on head CT or MR...

Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education.

Accurate interpretation of chest X-rays is a critical clinical skill, yet radiology training in medi...

Comparison of ChatGPT and dietitians in formulating diet plans and recommendations for patients with cardiometabolic diseases.

BACKGROUND: Artificial Intelligence (AI) is transforming personalized medicine, yet its efficacy con...

Radiomic Carotid Plaque Features Integrated into Machine Learning Models for Cardiovascular Risk Prediction.

OBJECTIVE: Carotid plaque detected by ultrasound is associated with major adverse cardiovascular eve...

Initial assessment of suspected cervical spine fracture in the emergency department: A contemporary narrative review.

Cervical spine fractures represent a potentially catastrophic consequence of blunt trauma. Early ide...