Strokes

mHealth-Enabled Stroke Screening for Pediatric Sickle Cell Disease in Low-Resource Settings: Systematic Literature Review of Critical Barriers, Emerging Technologies, and AI-Driven Solutions.

BACKGROUND: Sickle cell disease (SCD) is a genetic blood disorder affecting millions globally, with life-threatening complications, and most patients live in sub-Saharan Africa. Particularly, children with SCD have a high risk of stroke. Although early screening for stroke could help prevent many cases, access to effective stroke screening remains limited in low-resource settings (LRS). Existing t...

A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning.

Predicting the risk of stroke is one of the critical problems in healthcare, which necessitates efficient solutions for providing accurate and prompt risk assessments while preserving data confidentiality. This work proposes a new framework using Federated Learning (FL) to combine Multi-Layer Perceptron (MLP) and Gated Recurrent Unit (GRU) models that are essential in analyzing multimodal data. Im...

Beyond Conventional Imaging: From Time-Based to Data-Driven Decision-Making in Acute Ischemic Stroke.

Reperfusion therapy has profoundly transformed acute ischemic stroke (AIS) care. Initially, treatment decisions relied primarily on time from symptom ...

Association Between Environmental Chemicals Phthalates and Stroke Among the US Adults: A Cross-Sectional Study.

Stroke has a multifactorial etiology, and phthalates, as widely used environmental chemicals, may play an underexplored role in cerebrovascular health...

Multimorbidity and major adverse cardiovascular events in antipsychotic users: Time-to-event prediction by explainable machine learning.

Antipsychotic treatment is associated with higher risk of major adverse cardiovascular events (MACEs), and risk may vary by multimorbidity and concomi...

Prediction model for frailty risk in ischemic stroke patients: Application and validation of support vector machines and nomograms.

This study aimed to develop a prediction model based on nomograms and support vector machines (SVM) to assess frailty risk in ischemic stroke patients...

Periprocedural evaluation of patients with BAV stenosis undergoing TAVR: a machine learning-based study.

BACKGROUND: Transcatheter aortic valve replacement (TAVR) has increasingly emerged as one of the primary treatments for patients with severe bicuspid ...

Prediction of post-stroke brain swelling using biomechanical modelling and deep neural networks.

Malignant stroke is a life-threatening condition, with mortality rates reaching up to 80% among patients managed conservatively. Brain swelling volume...

Exploratory prediction model for deep vein thrombosis in intensive care patients after anticoagulation therapy: An observational study.

BACKGROUND: Despite low-molecular-weight heparin (LMWH) prophylaxis, the incidence of deep vein thrombosis (DVT) remains high in intensive care unit (...

Predicting one-year mortality risk in ICU patients with ischemic stroke using multi-algorithm machine learning and a nomogram.

Introduction: Ischemic stroke is a leading cause of mortality, and patients requiring intensive care unit (ICU) admission carry a guarded prognosis. W...

Predictive Value of Machine Learning for Poststroke Mortality Risk: Systematic Review and Meta-Analysis.

BACKGROUND: People with stroke face a high mortality risk, and an accurate prediction model is essential to the guidance of clinical decision-making i...

Retinal vascular phenotyping for early detection of coronary artery disease: quantitative assessment and diagnostic modelling.

OBJECTIVES: To investigate the association between quantitative retinal vascular parameters and coronary artery disease (CAD) and to evaluate the effi...

The Role of Machine Learning and Artificial Intelligence in Drug Discovery and Clinical Care of Pulmonary Hypertension.

Pulmonary hypertension (PH) is a severe and oftentimes fatal disease with a high degree of clinical variability. Its complexity necessitates a multifa...

Interpretable deep learning with pharmacogenomics predicts myocardial infarction in angiotensin receptor blocker treated hypertension.

OBJECTIVE: A significant residual risk of myocardial infarction (MI) persists in hypertensive patients treated with angiotensin receptor blockers (ARB...

Multimodal attention-enhanced network of segmenting acute ischemic stroke from perfusion images.

Acute ischemic stroke (AIS) is a major cause of long-term disability and mortality worldwide. Accurate segmentation of stroke lesions, particularly th...

Echocardiographic Evaluation of Global Cardiac Function in Systemic Sclerosis-Associated Pulmonary Arterial Hypertension: An Integrated Review of Parameters and Techniques.

Systemic sclerosis (SSc) is a connective tissue disease frequently complicated by pulmonary arterial hypertension (PAH), a leading cause of morbidity ...

A scalable EEG-based spatial neglect detection system in augmented reality for stroke patients.

BACKGROUND: Spatial neglect is a common visuospatial attention disorder following a stroke. To overcome weaknesses associated with classic pen-and-pap...

Retrieval-Augmented Language Models for Patient-Centered Periprocedural Anticoagulation in Interventional Radiology.

PURPOSE: This study evaluates Large Language Models (LLMs) integrated with Retrieval-Augmented Generation (RAG) frameworks for generating accurate, gu...

Deep learning-based non-contrast CT imaging markers enhance post-transfer DWI core volume prediction.

BACKGROUND: Deep learning enables the extraction of ischemic lesion size and hypodensity imaging markers from noncontrast CT (DLNCCT) in patients with...

Development and Validation of a Deep Learning-Based Facial Weakness Score for Objective Assessment in Facioscapulohumeral Muscular Dystrophy.

INTRODUCTION/AIMS: Facioscapulohumeral muscular dystrophy (FSHD) is a muscle disease that leads, among other manifestations, to facial weakness. This ...