Neurology

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

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The 'Sandwich' meta-framework for architecture agnostic deep privacy-preserving transfer learning for non-invasive brainwave decoding.

. Machine learning has enhanced the performance of decoding signals indicating human behaviour. Elec...

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. Char...

An Open-Source, User-Friendly Machine-Learning Method for Automated Segmentation and Analysis of Peripheral Nerve Cross-Sections.

BACKGROUND: Quantitative neuromorphometric analysis of the peripheral nerve is paramount to nerve re...

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 cerebrovascula...

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 morbid...

"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 Cogniti...

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...

Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment.

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are ...

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...

A neuromechanics solution for adjustable robot compliance and accuracy.

Robots have to adjust their motor behavior to changing environments and variable task requirements t...

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 d...

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 ...

Neuromorphic computing at scale.

Neuromorphic computing is a brain-inspired approach to hardware and algorithm design that efficientl...

Lean body mass and stroke volume, a sex issue.

INTRODUCTION: Large vessel occlusions (LVO) account for over 60% of stroke-related mortality and dis...

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 c...

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, ep...

Dual inhibition of AChE and MAO-B in Alzheimer's disease: machine learning approaches and model interpretations.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases. Given the multifac...

Recognition and classification of facial expression using artificial intelligence as a key of early detection in neurological disorders.

The recognition and classification of facial expressions using artificial intelligence (AI) presents...

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