Neurology

Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.

Electroencephalography (EEG) is invaluable in the management of acute neurological emergencies. Characteristic EEG changes have been identified in diverse neurologic conditions including stroke, trauma, and anoxia, and the increased utilization of continuous EEG (cEEG) has identified potentially harmful activity even in patients without overt clinical signs or neurologic diagnoses. Manual annotati...

Identification of lipid metabolism-related gene markers and construction of a diagnostic model for multiple sclerosis: An integrated analysis by bioinformatics and machine learning.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune inflammatory disorder that causes neurological disability. Dysregulated lipid metabolism contributes to the pathogenesis of MS. This study aimed to identify lipid metabolism-related gene markers and construct a diagnostic model for MS.

An interpretable hybrid machine learning approach for predicting three-month unfavorable outcomes in patients with acute ischemic stroke.

BACKGROUND: Acute ischemic stroke (AIS) is a clinical disorder caused by nontraumatic cerebrovascular disease with a high incidence, mortality, and di...

A novel hybrid ViT-LSTM model with explainable AI for brain stroke detection and classification in CT images: A case study of Rajshahi region.

Computed tomography (CT) scans play a key role in the diagnosis of stroke, a leading cause of morbidity and mortality worldwide. However, interpreting...

"Tom and Pepper Lab". Robotics for cognitive stimulation and social skills: A preliminary study.

Dementia affects over 55 million people globally, with early cognitive decline, such as Mild Cognitive Impairment (MCI), often preceding neurodegenera...

Multimodal multiview bilinear graph convolutional network for mild cognitive impairment diagnosis.

Mild cognitive impairment (MCI) is a significant predictor of the early progression of Alzheimer's disease (AD) and can serve as an important indicato...

Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.

Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Thus, the aim of the study was to identify bioma...

Palmitoylation-related gene ZDHHC22 as a potential diagnostic and immunomodulatory target in Alzheimer's disease: insights from machine learning analyses and WGCNA.

BACKGROUND: The mechanism of palmitoylation in the pathogenesis of Alzheimer's disease (AD) remains unclear.

Functional Disability and Psychological Impact in Headache Patients: A Comparative Study Using Conventional Statistics and Machine Learning Analysis.

: Recent research has focused on exploring the relationships between various factors associated with headaches and understanding their impact on indiv...

Neuromorphic computing at scale.

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficiently realizes artificial neural networks. Neuromorphi...

Prediction Trough Concentrations of Valproic Acid Among Chinese Adult Patients with Epilepsy Using Machine Learning Techniques.

OBJECTIVE: This study aimed to establish an optimal model based on machine learning (ML) to predict Valproic acid (VPA) trough concentrations in Chine...

Automated karyogram analysis for early detection of genetic and neurodegenerative disorders: a hybrid machine learning approach.

Anomalous chromosomes are the cause of genetic diseases such as cancer, Alzheimer's, Parkinson's, epilepsy, and autism. Karyotype analysis is the stan...

A data driven approach for soft tissue biomarker identification linked to Chiari-like malformation and syringomyelia.

Canine Chiari-like malformation (CM) is a neuroanatomical condition associated with conformational change of the cranium, craniocervical junction and ...

Lean body mass and stroke volume, a sex issue.

INTRODUCTION: Large vessel occlusions (LVO) account for over 60% of stroke-related mortality and disability. Lean body mass (LBM) represents metabolic...

Machine Learning-based Cluster Analysis Identifies Three Unique Phenotypes of Patients With Adult Spinal Deformity With Distinct Clinical Profiles and Long-term Recovery Trajectory: A Development Study.

STUDY DESIGN: A retrospective review of a prospective adult spinal deformity data.

[Application and considerations of artificial intelligence and neuroimaging in the study of brain effect mechanisms of acupuncture and moxibustion].

Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for...

Towards contrast-agnostic soft segmentation of the spinal cord.

Spinal cord segmentation is clinically relevant and is notably used to compute spinal cord cross-sectional area (CSA) for the diagnosis and monitoring...

Predicting lower body joint moments and electromyography signals using ground reaction forces during walking and running: An artificial neural network approach.

BACKGROUND: This study leverages Artificial Neural Networks (ANNs) to predict lower limb joint moments and electromyography (EMG) signals from Ground ...

Deep Clustering for Epileptic Seizure Detection.

UNLABELLED: Epilepsy is a neurological disorder characterized by recurrent epileptic seizures, which are often unpredictable and increase mortality an...

TFTL: A Task-Free Transfer Learning Strategy for EEG-Based Cross-Subject and Cross-Dataset Motor Imagery BCI.

OBJECTIVE: Motor imagery-based brain-computer interfaces (MI-BCIs) have been playing an increasingly vital role in neural rehabilitation. However, the...