Neurology

Machine Learning Analysis Using RNA Sequencing to Distinguish Neuromyelitis Optica from Multiple Sclerosis and Identify Therapeutic Candidates.

This study aims to identify RNA biomarkers distinguishing neuromyelitis optica (NMO) from relapsing-...

The promise of AI Large Language Models for Epilepsy care.

Artificial intelligence (AI) has been supporting our digital life for decades, but public interest i...

Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma.

INTRODUCTION: Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial i...

Biological matrices, reagents and turnaround-time: the full-circle of artificial intelligence in the pre-analytical Phase. Comment on Turcic A, et al., Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. CCLM 2024;62:436-41.

A hybrid EEG classification model using layered cascade deep learning architecture.

The problem of multi-class classification is always a challenge in the field of EEG (electroencephal...

Artificial intelligence-driven drug repositioning uncovers efavirenz as a modulator of α-synuclein propagation: Implications in Parkinson's disease.

Parkinson's disease (PD) is a complex neurodegenerative disorder with an unclear etiology. Despite s...

eDeeplepsy: An artificial neural framework to reveal different brain states in children with epileptic spasms.

OBJECTIVE: Despite advances, analysis and interpretation of EEG still essentially rely on visual ins...

A deep learning method to identify and localize large-vessel occlusions from cerebral digital subtraction angiography.

BACKGROUND AND PURPOSE: An essential step during endovascular thrombectomy is identifying the occlud...

Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis.

The ordered assembly of Tau protein into filaments characterizes Alzheimer's and other neurodegenera...

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

BACKGROUND: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals w...

Prediction of cognitive conflict during unexpected robot behavior under different mental workload conditions in a physical human-robot collaboration.

. Brain-computer interface (BCI) technology is poised to play a prominent role in modern work enviro...

Age-related changes in human brain functional connectivity using graph theory and machine learning techniques in resting-state fMRI data.

Aging is the basis of neurodegeneration and dementia that affects each endemic in the body. Normal a...

A novel predefined-time neurodynamic approach for mixed variational inequality problems and applications.

In this paper, we propose a novel neurodynamic approach with predefined-time stability that offers a...

Utilizing Siamese 4D-AlzNet and Transfer Learning to Identify Stages of Alzheimer's Disease.

Alzheimer's disease (AD) is the general form of dementia, leading to a progressive neurological diso...

Automated Detection of Cerebral Microbleeds on Two-dimensional Gradient-recalled Echo T2* Weighted Images Using a Morphology Filter Bank and Convolutional Neural Network.

PURPOSE: We present a novel algorithm for the automated detection of cerebral microbleeds (CMBs) on ...

Differential Diagnosis of Diabetic Foot Osteomyelitis and Charcot Neuropathic Osteoarthropathy with Deep Learning Methods.

Our study aims to evaluate the potential of a deep learning (DL) algorithm for differentiating the s...

Diffusion-/perfusion-weighted imaging fusion to automatically identify stroke within 4.5 h.

OBJECTIVES: We aimed to develop machine learning (ML) models based on diffusion- and perfusion-weigh...

Artificial intelligence predicts undiagnosed atrial fibrillation in patients with embolic stroke of undetermined source using sinus rhythm electrocardiograms.

BACKGROUND: Artificial intelligence (AI)-enabled sinus rhythm (SR) electrocardiogram (ECG) interpret...

Multimodal active subspace analysis for computing assessment oriented subspaces from neuroimaging data.

BACKGROUND: For successful biomarker discovery, it is essential to develop computational frameworks ...

Effect on functional outcome of robotic assisted rehabilitation versus conventional rehabilitation in patients with complete spinal cord injury: a prospective comparative study.

STUDY DESIGN: Prospective Comparative Study.

Artificial intelligence of imaging and clinical neurological data for predictive, preventive and personalized (P3) medicine for Parkinson Disease: The NeuroArtP3 protocol for a multi-center research study.

BACKGROUND: The burden of Parkinson Disease (PD) represents a key public health issue and it is esse...