Neurology

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 s...

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 ...

A Deep-Learning Empowered, Real-Time Processing Platform of fNIRS/DOT for Brain Computer Interfaces and Neurofeedback.

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time mon...

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 ...

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 enceph...

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. Identify...

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

BACKGROUND: Anterior lumbar interbody fusion is performed to fuse pathological spinal segments, gene...

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 heal...

Shared autonomy between human electroencephalography and TD3 deep reinforcement learning: A multi-agent copilot approach.

Deep reinforcement learning (RL) algorithms enable the development of fully autonomous agents that c...

Efficient Seizure Detection by Complementary Integration of Convolutional Neural Network and Vision Transformer.

Epilepsy, as a prevalent neurological disorder, is characterized by its high incidence, sudden onset...

Artificial intelligence applied to electroencephalography in epilepsy.

Artificial intelligence (AI) is progressively transforming all fields of medicine, promising substan...

Handwriting strokes as biomarkers for Alzheimer's disease prediction: A novel machine learning approach.

In recent years, machine learning-based handwriting analysis has emerged as a valuable tool for supp...

Advanced convolutional neural network with attention mechanism for Alzheimer's disease classification using MRI.

This paper introduces a novel convolutional neural network model with an attention mechanism to adva...

Flexible Patched Brain Transformer model for EEG decoding.

Decoding the human brain using non-invasive methods is a significant challenge. This study aims to e...

Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.

Driver drowsiness is a significant safety concern, contributing to numerous traffic accidents. To ad...

In Reply: Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.

Letter: Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.

The third wheel or the game changer? How AI could team up with neurologists in Parkinson's care.

INTRODUCTION: Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by diverse...

Comparative Assessment of Manual Segmentation of Cerebral Infarction Lesions in Experimental Animals Based on Magnetic Resonance Imaging Using Artificial Intelligence.

The aim of this study was to evaluate the quality of manual segmentation of cerebral infarction lesi...

External validation of a machine learning prediction model for massive blood loss during surgery for spinal metastases: a multi-institutional study using 880 patients.

BACKGROUND CONTEXT: A machine learning (ML) model was recently developed to predict massive intraope...

Electroencephalography Decoding with Conditional Identification Generator.

Decoding Electroencephalography (EEG) signals are extremely useful for advancing and understanding h...