Neurology

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

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Patterns of calcitonin gene-related peptide monoclonal antibody use in people with migraine: Results of the OVERCOME (US) study.

BackgroundUnderstanding characteristics and reasons associated with using calcitonin gene-related pe...

Tailoring neuromuscular dynamics: A modeling framework for realistic sEMG simulation.

This study introduces an advanced computational model for simulating surface electromyography (sEMG)...

Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.

Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) ac...

The Role of AI-driven Volumetric Aneurysm Analysis in the Management of Cerebral Aneurysms.

This article looks at the current state of aneurysm risk modeling, exploring the limitations of line...

Identification of neurological text markers associated with risk of stroke.

BACKGROUND: Delayed or missed stroke diagnosis is associated with poor outcomes. We utilized natural...

Multivariate and Machine Learning-Derived Virtual Staining and Biochemical Quantification of Cancer Cells through Raman Hyperspectral Imaging.

Advances in virtual staining and spatial omics have revolutionized our ability to explore cellular a...

Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.

Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucle...

POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.

. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG...

An ensemble-based 3D residual network for the classification of Alzheimer's disease.

Alzheimer's disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a ...

Current applications and outcomes of AI-driven adaptive learning systems in physical rehabilitation science education: A scoping review protocol.

Rationale Integrating artificial intelligence (AI) into education has introduced transformative poss...

Roman domination-based spiking neural network for optimized EEG signal classification of four class motor imagery.

The Spiking Neural Network (SNN) is a third-generation neural network recognized for its energy effi...

Deep learning enhanced deciphering of brain activity maps for discovery of therapeutics for brain disorders.

This study presents an artificial intelligence enhanced screening platform, DeepBAM, which enables ...

Artery fragment guided approach for enhancing cerebral aneurysm detection in TOF-MRA imaging.

BACKGROUND: Cerebral aneurysms are a type of cerebrovascular disease that poses a severe threat to l...

Enhancing differentiation between unipolar and bipolar depression through integration of machine learning and electroencephalogram analysis.

To enhance the differentiation between unipolar depression (UPD) and bipolar depression (BPD), this ...

Artificial Intelligence Deep Learning Models to Predict Spaceflight Associated Neuro-ocular Syndrome (SANS).

PURPOSE: To create deep learning artificial intelligence (AI) models for predicting the development ...

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone.

BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains ...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of...

Deep-learning-based Partial Volume Correction in 99mTc-TRODAT-1 SPECT for Parkinson's Disease: A Preliminary Study on Clinical Translation.

Tc-TRODAT-1 SPECT is effective for the early detection of Parkinson's disease (PD). However, SPECT i...

A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.

This study aims to enhance brain-computer interface (BCI) applications for individuals with motor im...

Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence.

Reliable classification of cognitive states in longitudinal Alzheimer's Disease (AD) studies is crit...

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