Neurology

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...

Towards a physics informed digital twin to predict cerebral blood flow and cerebral vascular regulation.

Cerebral blood flow is essential for brain function and is governed by cerebral perfusion pressure and physiological mechanisms collectively referred to as cerebral vascular regulation (CVR). Direct measurement of cerebral blood flow and individual CVR mechanisms is challenging and often unavailable clinically, limiting personalization of blood flow and CVR targets, particularly in neurological in...

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 ...

Alzheimer's disease staging using enhanced inception-ResNet-V2 and improved XceptionNet models for 3D MRI classification and segmentation.

BACKGROUND: Neurologists have a significant challenge due to the progressive nature of Alzheimer's disease (AD) and its severe effects on cognitive fu...

Predictive neuroimaging biomarkers of major depressive disorder treatment response: An umbrella review.

Major depressive disorder (MDD) is a heterogeneous condition with varied responses to pharmacological, psychotherapeutic, and neuromodulation interven...

LADNET: An MRI-based deep learning approach for Alzheimer's disease detection.

Alzheimer's disease (AD) is considered a leading form of dementia, found in the majority of individuals, and it accounts for the highest prevalence of...

Development of a deep learning tool to detect drug-resistance epilepsy with EEG.

INTRODUCTION: Drug-resistant epilepsy affects about 30% of patients and is linked to poorer outcomes. Deep learning can extract complex patterns from ...

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...

An enhanced framework for Parkinson's disease severity prediction using improved optimization in multi-scale TCN.

RESEARCH BACKGROUND: Parkinson's Disease (PD) requires accurate severity prediction models for enabling efficient treatment planning and disease manag...

Presence hallucination induction through robotically mediated somatomotor conflicts: A pooled analysis of 25 experiments.

Hallucinations are significant symptoms in psychiatric and neurodegenerative diseases, that may indicate advanced disease progression or worse disease...

Physiology-informed machine learning for patient-level surgical decision-making in severe traumatic brain injury: Multicenter model development with external validation.

BACKGROUND: Surgical decisions for severe traumatic brain injury (TBI) are often made under prognostic uncertainty. Existing prognostic models predict...

Acute spinal cord injury: contemporary management and novel therapies.

Acute spinal cord injury (SCI) remains a life-threatening condition associated with substantial morbidity, mortality, and long-term disability. Despit...

Use of natural language processing tools in musculoskeletal disability assessment: generating reports and calculating impairment percentages in Turkish health commission settings.

PURPOSE: This study explores the potential of Natural Language Processing (NLP) tools, specifically ChatGPT-4o and Data Analyst, in supporting health ...

CRISPR-Cas9 and next-generation gene editing strategies for therapeutic intervention of neurodegenerative pathways in Alzheimer's disease: a state-of-the-art review.

Alzheimer's disease (AD) is a progressive and multifactorial neurodegenerative disorder and the leading cause of dementia worldwide, characterized by ...

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...

A High-Quality Endoscopic Image Dataset with Annotated Recurrent Laryngeal Nerve for AI-Assisted Thyroid Surgery.

The integration of artificial intelligence (AI) into surgical navigation represents a pivotal advancement in modern operative medicine. In endoscopic ...

Single-channel EEG-based seizure prediction using deep learning.

Reliable seizure prediction can improve patient safety by enabling timely protective actions, yet most high-performing approaches depend on multichann...

Predicting Ketamine Exposure in Pediatric Status Epilepticus: A Pharmacokinetic-Machine Learning Approach.

BACKGROUND AND OBJECTIVE: Status epilepticus is a life-threatening neurological emergency. Ketamine combined with levetiracetam is a promising therapy...

Automatic lateral ventricle and choroid plexus segmentation method in infant brain MR images.

INTRODUCTION: Accurate segmentation of the lateral ventricles (LV) and choroid plexus (CP) in infant brain MRI is essential for understanding cerebros...