Neurology

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

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Identification of Spared and Proportionally Controllable Hand Motor Dimensions in Motor Complete Spinal Cord Injuries Using Latent Manifold Analysis.

The loss of bilateral hand function is a debilitating challenge for millions of individuals that suf...

Adapting Action Recognition Neural Networks for Automated Infantile Spasm Detection.

Infantile spasms are a severe epileptic syndrome characterized by short muscular contractions lastin...

Lumbar Radicular Pain in the Eyes of Artificial Intelligence: Can You 'Imagine' What I 'Feel'?

OBJECTIVE: Pain is a complex sensory and emotional experience that significantly impacts individuals...

The role of artificial intelligence in optimizing management of atrial fibrillation in acute ischemic stroke.

Atrial fibrillation (AF) is a severe condition associated with high morbidity and mortality, includi...

A computational and machine learning approach to identify GPR40-targeting agonists for neurodegenerative disease treatment.

The G protein-coupled receptor 40 (GPR40) is known to exert a significant influence on neurogenesis ...

Computational Fuzzy Modelling Approach to Analyze Neuronal Calcium Dynamics With Intracellular Fluxes.

Mathematical neuroscience investigates how calcium distribution in nerve cells affects the neurologi...

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.

BACKGROUND: Despite advances in neuro-oncology, treatments of glioma and tools for predicting the ou...

A Scoping Review of Machine Learning Applied to Peripheral Nerve Interfaces.

Peripheral nerve interfaces (PNIs) can enable communication with the peripheral nervous system and h...

Interpretable machine learning model for predicting the prognosis of antibody positive autoimmune encephalitis patients.

OBJECTIVE: The objective was to utilize nine machine learning (ML) methods to predict the prognosis ...

A deep learning approach for non-invasive Alzheimer's monitoring using microwave radar data.

Over 50 million people globally suffer from Alzheimer's disease (AD), emphasizing the need for effic...

Deep Learning Detection of Hand Motion During Microvascular Anastomosis Simulations Performed by Expert Cerebrovascular Neurosurgeons.

OBJECTIVE: Deep learning enables precise hand tracking without the need for physical sensors, allowi...

Assessment of wearable robotics performance in patients with neurological conditions.

PURPOSE OF REVIEW: While wearable robotics is expanding within clinical settings, particularly for n...

Effects of end-effector robotic arm reach training with functional electrical stimulation for chronic stroke survivors.

BACKGROUND: Upper-extremity dysfunction significantly affects dependence in the daily lives of strok...

Controversies in Artificial Intelligence in Neurosurgery.

Artificial intelligence (AI) has evolved from science fiction to a technology infiltrating everyday ...

Machine learning based classification of excessive smartphone users via neuronal cue reactivity.

Excessive Smartphone Use (ESU) poses a significant challenge in contemporary society, yet its recogn...

Deep Neural Network-Based Empirical Mode Decomposition for Motor Imagery EEG Classification.

Motor imagery refers to the brain's response during the mental simulation of physical activities, wh...

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI ...

Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks.

In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a formidable ...

Deep learning model for automated diagnosis of degenerative cervical spondylosis and altered spinal cord signal on MRI.

BACKGROUND CONTEXT: A deep learning (DL) model for degenerative cervical spondylosis on MRI could en...

A Lightweight Convolutional Neural Network-Reformer Model for Efficient Epileptic Seizure Detection.

A real-time and reliable automatic detection system for epileptic seizures holds significant value i...

MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Structural and functional brain networks are generated from two scan sequences of magnetic resonance...

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