Strokes

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 nongated, noncontrast chest CT beyond conventional risk stratification remains uncertain. We evaluated whether AI-derived EAT volume and attenuation improve prediction of major adverse cardiovascular events (MACE) beyond clinical risk factors and coronary artery calcium (CAC) in an asymptomatic screening ...

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-consuming and often requires contrast injection for perfusion imaging. This study aimed to synthesize T-map perfusion maps from routinely available, noncontrast diffusion-weighted imaging and fluid-attenuated inversion recovery using deep generative models. We hypothesized that relevant perfusion infor...

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) systems versus that of radiologists on routine clinical...

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 effective clinical management. Magnetic resonance ang...

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 critical medical emergency requiring rapid and accurate d...

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 cardiovascular and metabolic dysfunction. To elucidate the in...

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

AIM: Machine learning (ML) applications in pharmacovigilance remain limited and underexplored. Using data from the French National pharmacovigilance d...

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-assisted gait training (RAGT) with an exoskeletal w...

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-resolution with subsequent image processing to genera...

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 latent class analysis (LCA) and assess their generaliza...

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 perigastric veins on contrast-enhanced CT images in patie...

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 anticoagulant (OAC) therapy. CHA₂DS₂-VASc cannot predict...

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 environmental toxicants increasingly recognized as nont...

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 MRI scanning. We aim to develop classifier models to...

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 medical education remains limited and often fails to p...

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 constitutes a dynamic factor in the field of health a...

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 events (MACE) and can be characterized using manual o...

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 identification of unstable injuries is critical to pr...

Clinical relevance of intracranial stenosis as false-positive findings of a deep learning algorithm trained to detect large vessel occlusions: a retrospective cohort study of a supraregional stroke centre.

OBJECTIVES: This study aims to explore the ability to identify high-grade intracranial arterial stenosis (ICAS) by an artificial intelligence (AI) des...