Neurology

Speech production as an artificial intelligence-based 'process' measure of cognition sensitive to mild cognitive impairment and Alzheimer's disease.

Process scores in neuropsychological tests add incremental validity for detecting non-normative cognitive aging trajectories. However, process scores are laborious and time-consuming to derive. Using AI-driven natural language processing, we investigated objective speech markers related to speech production as a potential process score for measuring cognition, identifying mild cognitive impairmen...

Feature selection using metaheuristics to predict annual amyotrophic lateral sclerosis progression.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease with no curative treatment and affecting motor neurons, leads to motor weakness, atrophy, spasticity and difficulties with speech, swallowing, and breathing. Accurately predicting disease progression and survival is crucial for optimizing patient care, intervention planning, and informed decision-making.

EEG Signal Analysis for the Classification of Alzheimer's and Frontotemporal Dementia: A Novel Approach Using Artificial Neural Networks and Cross-Entropy Techniques.

Dementia, a neurological disorder, can cause cognitive decline due to damage to the brain. Our study aims to contribute to the development of computer...

Micrographia in Parkinson's Disease: Automatic Recognition through Artificial Intelligence.

BACKGROUND: Parkinson's disease (PD) leads to handwriting abnormalities primarily characterized by micrographia. Whether micrographia manifests early ...

Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the ag...

Identification and validation of glucocorticoid receptor and programmed cell death-related genes in spinal cord injury using machine learning.

Spinal cord injury (SCI) is a severe neurological disorder, with glucocorticoids like methylprednisolone commonly used for treatment. However, their e...

Prediction of Motor Symptom Progression of Parkinson's Disease Through Multimodal Imaging-Based Machine Learning.

The unrelenting progression of Parkinson's disease (PD) leads to severely impaired quality of life, with considerable variability in progression rates...

Evaluation of retrieval-augmented generation and large language models in clinical guidelines for degenerative spine conditions.

PURPOSE: Degenerative spinal diseases often require complex, patient-specific treatment, presenting a compelling challenge for artificial intelligence...

Optimal multimodal feature combination and classifier selection for music-based EEG signal analysis.

PURPOSE: Music perception is a fundamental human experience, integral to cognitive and emotional processing, making it a crucial area for neuroscienti...

Automated facial nerve identification in microsurgery with an improved unet.

To develop a deep-learning model that improves the segmentation and detection of Facial Nerve in microsurgery, thereby increasing surgical precision a...

Biomimetic Analysis of Neurotransmitters for Disease Diagnosis through Light-Driven Nanozyme Sensor Array and Machine Learning.

Neurological diseases, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, pose a significant global health challenge due to t...

Identification of inflammation-related biomarkers and therapeutic targets for neurogenic bladder fibrosis via multi-omics analysis.

Inflammatory responses play a crucial role in the progression of pediatric neurogenic bladder (NB)-associated fibrosis; however, their specific contri...

MRI-based detection of multiple sclerosis using an optimized attention-based deep learning framework.

BACKGROUND: Multiple Sclerosis (MS) is a chronic neurological disorder affecting millions worldwide. Early detection is vital to prevent long-term dis...

A systematic review of machine learning approaches in cochlear implant outcomes.

Cochlear implants (CIs) have transformed the lives of over one million individuals with hearing impairment, including children as young as nine months...

Neuroimaging and biological markers of different paretic hand outcomes after stroke.

BACKGROUND: Hand dysfunction significantly affects independence after stroke, with outcomes varying across individuals. Exploring biomarkers associate...

Effects of device-assisted practice of activities of daily living in a close-to-normal pattern on upper extremity motor recovery in individuals with moderate to severe stroke: study protocol of a randomized control trial.

BACKGROUND: The majority of individuals with chronic stroke have residual upper extremity (UE) disability which they cite as their greatest barrier to...

Exploratory development of human-machine interaction strategies for post-stroke upper-limb rehabilitation.

BACKGROUND: Stroke and its related complications, place significant burdens on human society in the twenty-first century, and lead to substantial dema...

Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention.

Targeted muscle reinnervation (TMR) was initially developed as a technique for bionic prosthetic control but has since become a widely adopted strateg...

Mutualistic Multi-Network Noisy Label Learning (MMNNLL) Method and Its Application to Transdiagnostic Classification of Bipolar Disorder and Schizophrenia.

The subjective nature of diagnosing mental disorders complicates achieving accurate diagnoses. The complex relationship among disorders further exacer...

Identifying features of prior hemorrhage in cerebral cavernous malformations on quantitative susceptibility maps: a machine learning pilot study.

Features of new bleeding on conventional imaging in cerebral cavernous malformations (CCMs) often disappear after several weeks, yet the risk of reble...