Neurology

LGFormer: integrating local and global representations for EEG decoding.

Electroencephalography (EEG) decoding is challenging because of its temporal variability and low signal-to-noise ratio, which complicate the extraction of meaningful information from signals. Although convolutional neural networks (CNNs) effectively extract local features from EEG signals, they are constrained by restricted receptive fields. In contrast, transformers excel at capturing global depe...

5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression.

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to apply network biology and machine learning (ML) techniques to identify novel repurposed drug candidates for ALS. In this study, we conducted a meta-a...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest is critical for informed clinical decision-making...

An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental h...

Deep learning assisted retinal microvasculature assessment and cerebral small vessel disease in Fabry disease.

PURPOSE: The aim of this study was to assess retinal microvascular parameters (RMPs) in Fabry disease (FD) using deep learning, and analyze the correl...

Does infection alter antifungal distribution: an animal model exploring pharmacokinetic changes.

AIM: Assessing the disseminated meningitis caused by in Wistar rats and its impact on antifungal distribution by microdialysis (µD).

A Quantitative Study of Factors Influencing Myasthenia Gravis Telehealth Examination Score.

INTRODUCTION/AIMS: The adoption of telemedicine is generally considered as advantageous for patients and physicians, but there is limited rigorous ass...

Automated segmentation of the dorsal root ganglia in MRI.

The dorsal root ganglion (DRG) contains all primary sensory neurons, but its functional role in somatosensory and pain processing remains unclear. Rec...

An innovative model based on machine learning and fuzzy logic for tracking lower limb exercises in stroke patients.

Rehabilitation after a stroke is vital for regaining functional abilities. However, a shortage of rehabilitation professionals leads to many patients ...

Hardware Optimization and Implementation of a 16-Channel Neural Tree Classifier for On-Chip Closed-Loop Neuromodulation.

This work presents the development of on-chip machine learning (ML) classifiers for implantable neuromodulation system-on-chips (SoCs), aimed at detec...

Updates in Neonatal Seizures.

Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ...

Artificial intelligence applied to epilepsy imaging: Current status and future perspectives.

In recent years, artificial intelligence (AI) has become an increasingly prominent focus of medical research, significantly impacting epileptology as ...

Identifying progression subphenotypes of Alzheimer's disease from large-scale electronic health records with machine learning.

OBJECTIVE: Identification of clinically meaningful subphenotypes of disease progression can enhance the understanding of disease heterogeneity and und...

Detecting cognitive impairment in cerebrovascular disease using gait, dual tasks, and machine learning.

BACKGROUND: Cognitive impairment is common after a stroke, but it can often go undetected. In this study, we investigated whether using gait and dual ...

Can we use lower extremity joint moments predicted by the artificial intelligence model during walking in patients with cerebral palsy in the clinical gait analysis?

Several studies have highlighted the advantages of employing artificial intelligence (AI) models in gait analysis. However, the credibility and practi...

Structural and metabolic topological alterations associated with butylphthalide treatment in mild cognitive impairment: Data from a randomized, double-blind, placebo-controlled trial.

AIMS: Effective intervention for mild cognitive impairment (MCI) is key for preventing dementia. As a neuroprotective agent, butylphthalide has the po...

Unsupervised Deep Learning of Electronic Health Records to Characterize Heterogeneity Across Alzheimer Disease and Related Dementias: Cross-Sectional Study.

BACKGROUND: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essenti...

Detection of antibodies in suspected autoimmune encephalitis diseases using machine learning.

In our study, we aim to predict the antibody serostatus of patients with suspected autoimmune encephalitis (AE) using machine learning based on pre-co...

Influence pathways of noise exposure on people's negative emotions and health across different activity contexts: A neural network-based double machine learning approach.

Noise is a major global environmental issue that raises concerns about both mental and physical health. However, few studies have investigated the med...

A modular cage may prevent endplate damage and improve spinal deformity correction.

BACKGROUND: Anterior lumbar interbody fusion is performed to fuse pathological spinal segments, generally, with a monobloc cage inserted by impact for...