Strokes

Deep learning-based cognitive impairment brain imaging analysis: New methods, new technologies, and new paradigms.

Cognitive impairment arising from ischemic stroke, Alzheimer's disease, and Parkinson's disease presents distinct structural and network-level alterations. Brain magnetic resonance imaging offers a non-invasive and high-resolution approach to assess these changes, while deep learning provides powerful tools for automated analysis. Given that accurate lesion delineation, precise localization of abn...

Performance of an automated Korotkoff sound BP monitor in cardiovascular patients.

OBJECTIVES: Accurate blood pressure measurement is essential for cardiovascular risk management, but conventional oscillometric devices are unreliable for atrial fibrillation and arterial stiffness. We evaluated the accuracy of a novel automated Korotkoff sound-based monitor (Ksens-BP), integrating a semiconductor strain-gauge sensor and artificial intelligence waveform classification, compared to...

Spatial radiomics-based interpretable multimodal machine learning model enhances outcomes prediction for minor stroke: A multicenter cohort study.

INTRODUCTION: Accurately identifying unfavorable outcomes is crucial for the clinical management of minor stroke. Conventional imaging prediction mode...

Machine learning in stroke and its sequelae: a narrative review of clinical applications and emerging trends.

OBJECTIVE: This narrative review synthesizes machine learning (ML) applications across the stroke and post-stroke continuum from acute imaging and dia...

Factors Impacting the Performance of Deep Learning Detection of Pulmonary Emboli.

OBJECTIVE: AI models are increasingly adopted in clinical practice, yet their generalizability outside controlled validation settings remains unclear....

Fully automated quantification of net water uptake in acute ischemic stroke using only non-contrast CT imaging.

OBJECTIVE: Estimating early lesion progression in ischemic stroke is essential for assessing thrombolytic treatment efficacy. While computed tomograph...

A clinical-AI correlation for integrating artificial intelligence into stroke care: a systematized literature review and practice framework.

BACKGROUND AND PURPOSE: The rapid integration of artificial intelligence (AI) into stroke care has outpaced many clinicians' ability to critically eva...

The global epidemiology of botulism outbreaks from open-source intelligence, 2017-2024.

BACKGROUND: Botulism is a rare but potentially fatal illness caused by botulinum neurotoxin, with outbreaks reported globally in humans, animals, and ...

Artificial intelligence for atrial fibrillation prediction while in sinus rhythm: a narrative review.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia affecting over 50 million individuals worldwide, with serious complications including strok...

Comparative Performance of Machine Learning and Traditional Risk Scores in Predicting Adverse Events After Transcatheter Aortic Valve Replacement in Patients With Atrial Fibrillation.

Patients with atrial fibrillation (AF) following transcatheter aortic valve replacement (TAVR) remain at risk of ischemic stroke (IS) and bleeding. Ho...

Efficacy and comparative performance of machine learning models for stroke risk prediction in hypertensive patients: A systematic review and meta-analysis.

BACKGROUND: Stroke poses a significant health burden among hypertensive patients, where traditional risk models often lack precision. Machine learning...

Creating Solutions to the Challenges in Managing Heparin Infusions: Exploring Use of Technology Including Artificial Intelligence and the Role of Anticoagulation Stewardship.

Adverse events and medication-related errors are common with anticoagulants. Heparin infusions are frequently associated with administration errors or...

Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis.

Carotid artery stenosis (CAS) is a major contributor to ischemic stroke, and molecular tools for its early detection remain limited. To address this n...

Clinical Phenotypes in Hypertension: A Data-Driven Approach to Risk Stratification.

BACKGROUND: Hypertension is a major contributor to cardiovascular morbidity and mortality. Its heterogeneity complicates risk stratification. Unsuperv...

Toward AI-Powered Neurovascular Intervention: From Imaging to XR-Robotic Convergence.

Stroke remains a leading cause of mortality and long-term disability worldwide, where rapid diagnosis and timely intervention are critical for improvi...

Value of Multiomics Over Clinical Risk Factors in Hypertension Prediction.

BACKGROUND: Several omics methods have been successfully used in hypertension prediction. However, the predictive ability of various multiomics data h...

Quantitative CT of emphysema, wall thickness and mucus plugs in alpha-1-antitrypsin deficiency: relationship to clinical outcomes.

OBJECTIVES: Alpha-1-antitrypsin deficiency (AATD) is a rare genetic disorder leading to chronic obstructive pulmonary disease (COPD). Emphysema is the...

Machine learning models for CT-based classification of ischemic stroke onset time within or beyond 4.5 h: a comparison of approaches.

OBJECTIVES: To compare machine learning models using different combinations of clinical and imaging variables for classifying ischemic stroke patients...

Predictors of glycemic control with imeglimin for type 2 diabetes: Results of machine learning analyses using clinical trial data.

INTRODUCTION: Identifying patient characteristics predictive of treatment response is crucial for optimizing type 2 diabetes outcomes. Using data from...

Enlarged Perivascular Spaces and Modifiable Vascular Risk Factors: Cross-Sectional and Longitudinal Analysis in the UK Biobank Cohort.

OBJECTIVE: To examine cross-sectional and longitudinal associations between vascular risk factors, APOE genotype, and perivascular spaces (PVS), with ...