Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Identification of disulfidptosis-related genes and subgroups in spinal cord injury.

STUDY DESIGN: Bioinformatics analysis and experimental validation study.

Cognitive impairment screening strategy to reduce the burden of Alzheimer's disease in Shanghai: A system dynamics approach.

BACKGROUND: Population aging increases the economic burden of Alzheimer's disease (AD). Early screen...

Development of a Novel Machine Learning Model to Automate Vertebral Column Segmentation Utilizing Biplanar Full-body Imaging.

BACKGROUND CONTEXT: Degenerative scoliosis (DS) is a common spinal disorder among adults, characteri...

Effects of exoskeleton rehabilitation robot training on neuroplasticity and lower limb motor function in patients with stroke.

BACKGROUND: Lower limb exoskeleton rehabilitation robot is a new technology to improve the lower lim...

Multi-modal signal integration for enhanced sleep stage classification: Leveraging EOG and 2-channel EEG data with advanced feature extraction.

This paper introduces an innovative approach to sleep stage classification, leveraging a multi-modal...

Optimizing stroke lesion segmentation: A dual-approach using Gaussian mixture models and nnU-Net.

Machine learning-based stroke lesion segmentation models are widely used in biomedical imaging, but ...

Ensemble Learning-Based Alzheimer's Disease Classification Using Electroencephalogram Signals and Clock Drawing Test Images.

Ensemble learning (EL), a machine learning technique that combines the results of multiple learning ...

Role and Potential of Artificial Intelligence in Biomarker Discovery and Development of Treatment Strategies for Amyotrophic Lateral Sclerosis.

Neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), present significant chall...

Assessing the Content of Goals of Care Documentation for Hospitalized Patients With Alzheimer's Disease and Related Dementias.

BACKGROUND: Goals of care (GOC) conversations are an evidence-based practice that help clarify and a...

Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.

Electroencephalography (EEG) is one of the most used techniques to perform diagnosis of epilepsy. Ho...

A depression detection approach leveraging transfer learning with single-channel EEG.

Major depressive disorder (MDD) is a widespread mental disorder that affects health. Many methods co...

Deep Learning-Based Algorithm for Automatic Quantification of Nigrosome-1 and Parkinsonism Classification Using Susceptibility Map-Weighted MRI.

BACKGROUND AND PURPOSE: The diagnostic performance of deep learning model that simultaneously detect...

Empowering Data Sharing in Neuroscience: A Deep Learning Deidentification Method for Pediatric Brain MRIs.

BACKGROUND AND PURPOSE: Privacy concerns, such as identifiable facial features within brain scans, h...

Conditional Generative Models for Simulation of EMG During Naturalistic Movements.

Numerical models of electromyography (EMG) signals have provided a huge contribution to our fundamen...

Data alignment based adversarial defense benchmark for EEG-based BCIs.

Machine learning has been extensively applied to signal decoding in electroencephalogram (EEG)-based...

Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology.

OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited...

Significance of NMDA receptor-targeting compounds in neuropsychological disorders: An in-depth review.

N-methyl-D-aspartate receptors (NMDARs), a subclass of glutamate-gated ion channels, play an integra...

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