Neurology

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

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UDBRNet: A novel uncertainty driven boundary refined network for organ at risk segmentation.

Organ segmentation has become a preliminary task for computer-aided intervention, diagnosis, radiati...

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.

Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises...

Multimodal deep learning for dementia classification using text and audio.

Dementia is a progressive neurological disorder that affects the daily lives of older adults, impact...

EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.

Pattern recognition based on network connections has recently been applied to the brain-computer int...

Detection of Parkinson disease using multiclass machine learning approach.

Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive ...

Analysis of convolutional neural networks for fronto-temporal dementia biomarker discovery.

PURPOSE: Frontotemporal lobe dementia (FTD) results from the degeneration of the frontal and tempora...

Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach.

Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized ...

GCTNet: a graph convolutional transformer network for major depressive disorder detection based on EEG signals.

Identifying major depressive disorder (MDD) using objective physiological signals has become a press...

sMRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.

OBJECTIVE: To establish a multi-dimensional representation solely on structural MRI (sMRI) for early...

Artificial intelligence-based automatic nidus segmentation of cerebral arteriovenous malformation on time-of-flight magnetic resonance angiography.

OBJECTIVE: Accurate nidus segmentation and quantification have long been challenging but important t...

Upper Limb Robots for Recovery of Motor Arm Function in Patients With Stroke: A Systematic Review and Meta-Analysis.

BACKGROUND AND OBJECTIVES: Robot technology to support upper limb (UL) rehabilitation poststroke has...

Text-to-video generative artificial intelligence: sora in neurosurgery.

Artificial intelligence (AI) has increased in popularity in neurosurgery, with recent interest in ge...

An optimized EEGNet decoder for decoding motor image of four class fingers flexion.

As a cutting-edge technology of connecting biological brain and external devices, brain-computer int...

Artificial Intelligence in Otology and Neurotology.

Clinical applications of artificial intelligence (AI) have grown exponentially with increasing compu...

A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network.

BACKGROUND: Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can l...

Machine learning for predicting hematoma expansion in spontaneous intracerebral hemorrhage: a systematic review and meta-analysis.

PURPOSE: Early identification of hematoma enlargement and persistent hematoma expansion (HE) in pati...

Post-stroke hand gesture recognition via one-shot transfer learning using prototypical networks.

BACKGROUND: In-home rehabilitation systems are a promising, potential alternative to conventional th...

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