Neurology

Machine-Learning-Powered Neural Interfaces for Smart Prosthetics and Diagnostics

Advanced neural interfaces are transforming applications ranging from neuroscience research to diagnostic tools (for mental state recognition, tremor and seizure detection) as well as prosthetic devices (for motor and communication recovery). By integrating complex functions into miniaturized neural devices, these systems unlock significant opportunities for personalized assistive technologies a...

Machine Learning Scoring Reveals Increased Frequency of Falls Proximal to Death in Drosophila melanogaster.

Falls are a significant cause of human disability and death. Risk factors include normal aging, neurodegenerative disease, and sarcopenia. Drosophila melanogaster is a powerful model for study of normal aging and for modeling human neurodegenerative disease. Aging-associated defects in Drosophila climbing ability have been observed to be associated with falls, and immobility due to a fall is impli...

Ethics From the Outset: Incorporating Ethical Considerations into the Artificial Intelligence and Technology Collaboratories for Aging Research Pilot Projects.

There is an urgent need to develop tools to enable older adults to live healthy, independent lives for as long as possible. To address this need, the ...

PointExplainer: Towards Transparent Parkinson's Disease Diagnosis

Deep neural networks have shown potential in analyzing digitized hand-drawn signals for early diagnosis of Parkinson's disease. However, the lack of...

Closed-loop control of seizure activity via real-time seizure forecasting by reservoir neuromorphic computing

Closed-loop brain stimulation holds potential as personalized treatment for drug-resistant epilepsy (DRE) but still suffers from limitations that re...

Monitoring morphometric drift in lifelong learning segmentation of the spinal cord

Morphometric measures derived from spinal cord segmentations can serve as diagnostic and prognostic biomarkers in neurological diseases and injuries...

Low-dimensional representation of brain networks for seizure risk forecasting

Identifying preictal states -- periods during which seizures are more likely to occur -- remains a central challenge in clinical computational neuro...

KnowEEG: Explainable Knowledge Driven EEG Classification

Electroencephalography (EEG) is a method of recording brain activity that shows significant promise in applications ranging from disease classificat...

A Methodological and Structural Review of Parkinsons Disease Detection Across Diverse Data Modalities

Parkinsons Disease (PD) is a progressive neurological disorder that primarily affects motor functions and can lead to mild cognitive impairment (MCI...

A Comprehensive Review of Arachnoid Cysts.

Arachnoid cysts are cerebrospinal fluid (CSF)-filled sacs that develop within the arachnoid membrane surrounding the brain or spinal cord, often remai...

Preparing for Vascular Surgery Board Certification: A Comparative Study Using Large Language Models.

Introduction and aim Large language models (LLMs) are transforming medical education by offering innovative methods to enhance teaching and learning. ...

Adaptive Dynamic Surface Control of Epileptor Model Based on Nonlinear Luenberger State Observer.

Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, which are sudden bursts of electrical activity in the brain. The Ep...

NDDRF 2.0: An update and expansion of risk factor knowledge base for personalized prevention of neurodegenerative diseases.

INTRODUCTION: Neurodegenerative diseases (NDDs) are chronic diseases caused by brain neuron degeneration, requiring systematic integration of risk fac...

Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.

INTRODUCTION: Post-stroke depression is one of the important complications of stroke and affects patients' quality of life. Early identification of po...

Mental state classification based on electroencephalogram (EEG) using multiclass support vector machine.

INTRODUCTION: Mental state refers to a person's state of mind from various perspectives, including consciousness, intention, and functionalism. Mental...

Research Focus Involving and Trends in Artificial Intelligence for Spinal Pain: A Bibliometric Analysis.

BACKGROUND: Spinal pain is a pervasive global health issue that poses significant challenges because of the disability and economic burden it causes. ...

Automated Imaging Differentiation for Parkinsonism.

IMPORTANCE: Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation ...

Evaluating Performance of a Deep Learning Multilabel Segmentation Model to Quantify Acute and Chronic Brain Lesions at MRI after Stroke and Predict Prognosis.

Purpose To develop and evaluate a multilabel deep learning network to identify and quantify acute and chronic brain lesions at multisequence MRI after...

Dual-stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation.

INTRODUCTION: Identifying individuals with dementia is crucial for prevalence estimation and service planning, but reliable, scalable methods are lack...

Early detection of Alzheimer's disease using deep learning methods.

INTRODUCTION: Alzheimer's disease (AD), a leading cause of dementia, requires early detection for effective intervention. This study employs AI to ana...