Neurology

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

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Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.

Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) ...

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to...

Significance of plasma p-tau217 in predicting long-term dementia risk in older community residents: Insights from machine learning approaches.

INTRODUCTION: Whether plasma biomarkers play roles in predicting incident dementia among the general...

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.

BACKGROUND: With the rise of artificial intelligence (AI) in the field of dementia biomarker researc...

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurat...

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

Human listeners have the ability to direct their attention to a single speaker in a multi-talker env...

A review: artificial intelligence in image-guided spinal surgery.

INTRODUCTION: Due to the complex anatomy of the spine and the intricate surgical procedures involved...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) play...

A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs.

Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of...

Graph Embedded Ensemble Deep Randomized Network for Diagnosis of Alzheimer's Disease.

Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist ...

A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.

Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the...

Ensemble Deep Random Vector Functional Link Network Using Privileged Information for Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) is a progressive brain disorder. Machine learning models have been proposed...

Single-Task and Dual-Task Gait Performance After Sport-Related Concussion: A Machine Learning Statistical Approach.

BACKGROUND: This study evaluated 2 different dual-task (DT) conditions during tandem gait (TG) to pr...

Contrastive machine learning reveals Parkinson's disease specific features associated with disease severity and progression.

Parkinson's disease (PD) exhibits heterogeneity in terms of symptoms and prognosis, likely due to di...

Accuracy of thoracic nerves recognition for surgical support system using artificial intelligence.

We developed a surgical support system that visualises important microanatomies using artificial int...

Duple-MONDNet: duple deep learning-based mobile net for motor neuron disease identification.

BACKGROUND/AIM: Motor neuron disease (MND) is a devastating neuron ailment that affects the motor ne...

Artificial intelligence prediction of In-Hospital mortality in patients with dementia: A multi-center study.

BACKGROUND: Prediction of mortality is very important for care planning in hospitalized patients wit...

Generative artificial intelligence versus clinicians: Who diagnoses multiple sclerosis faster and with greater accuracy?

BACKGROUND: Those receiving the diagnosis of multiple sclerosis (MS) over the next ten years will pr...

Automatically Extracting and Utilizing EEG Channel Importance Based on Graph Convolutional Network for Emotion Recognition.

Graph convolutional network (GCN) based on the brain network has been widely used for EEG emotion re...

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