Neurology

The clinical significance of an AI-based assumption model for neurocognitive diseases using a novel dual-task system.

Dual-task composed of gait or stepping tasks combined with cognitive tasks has been well-established as valuable tools for detecting neurocognitive disorders such as mild cognitive impairment and early-stage Alzheimer's disease. We previously developed a novel dual-task system with high accuracy for differentiating patients with neurocognitive disorders from healthy controls. In this study, we aim...

Revolutionizing Alzheimer's disease detection with a cutting-edge CAPCBAM deep learning framework.

Early and accurate diagnosis of Alzheimer's disease (AD) is crucial for effective treatment. While the integration of deep learning techniques for AD classification is not entirely new, this study introduces CAPCBAM-a framework that extends prior approaches by combining Capsule Networks with a Convolutional Block Attention Module (CBAM). In CAPCBAM, standardized preprocessing of MRI images is foll...

A wearable ankle-assisted robot for improving gait function and pattern in stroke patients.

BACKGROUND: Hemiplegic gait after a stroke can result in a decreased gait speed and asymmetrical gait pattern. Normal gait patterns and speed are typi...

A Multimodal Approach for Early Identification of Mild Cognitive Impairment and Alzheimer's Disease With Fusion Network Using Eye Movements and Speech.

Detecting Alzheimer's disease (AD) in its earliest stages, particularly during an onset of Mild Cognitive Impairment (MCI), remains challenging due to...

Social robot PIO intervention for improving cognitive function and depression in older adults with mild to moderate dementia in day care centers: A randomized controlled trial.

The increases in the older population, the prevalence of dementia, and the resulting social costs are burdensome to individuals, families, and the nat...

Epileptic seizure detection in EEG signals using deep learning: LSTM and bidirectional LSTM.

This paper established a new automatic method to detect epileptic seizures in EEG signals based on discret wavelet transform (DWT) and Deep Learning (...

AO Spine Clinical Practice Recommendations for Diagnosis and Management of Degenerative Cervical Myelopathy: Evidence Based Decision Making - A Review of Cutting Edge Recent Literature Related to Degenerative Cervical Myelopathy.

Study DesignLiterature review of key topics related to degenerative cervical myelopathy (DCM) with critical appraisal and clinical recommendations.Obj...

Initial seizure episodes risk factors identification during hospitalization of ICU patients: A retrospective analysis of the eICU collaborative research database.

BACKGROUND: We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

Machine Learning-Enhanced Cerebrospinal Fluid N-Glycome for the Diagnosis and Prognosis of Primary Central Nervous System Lymphoma.

The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant challenges. In this study, the potential use of mac...

Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.

A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia, yet the spatial variability...

Electrochemical activation of alum sludge for the adsorption of lead (Pb(II)) and arsenic (As): Mechanistic insights and machine learning (ML) analysis.

Alum sludge (AlS) has emerged as an effective adsorbent for anionic contaminants, with traditional activation methods like acid/base treatments and ca...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cogn...

Intersecting impact of CAG repeat and huntingtin knockout in stem cell-derived cortical neurons.

Huntington's Disease (HD) is caused by a CAG repeat expansion in the gene encoding huntingtin (HTT). While normal HTT function appears impacted by the...

Machine Learning-Based Diagnostic Prediction Model Using T1-Weighted Striatal Magnetic Resonance Imaging for Early-Stage Parkinson's Disease Detection.

RATIONALE AND OBJECTIVES: Diagnosing Parkinson's disease (PD) typically relies on clinical evaluations, often detecting it in advanced stages. Recentl...

AI-assisted identification of disability patterns within identical EDSS grades.

BACKGROUND: The Neurostatus-Expanded Disability Status Scale (EDSS) is the most frequently used measure of disability in multiple sclerosis (MS) trial...

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for post-stroke rehabilitation. The system utilizes elect...

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting in impairments in motor control, cognition, and e...

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.

BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, wheth...

Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods.

Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteries from similar signal intensity and proximity (i....

Deep learning-based EEG source imaging is robust under varying electrode configurations.

OBJECTIVES: Previous research has underscored the necessity of high-density EEG for accurate and reliable EEG source imaging (ESI) results with conven...