Neurology

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

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Prognostic value of multi-PLD ASL radiomics in acute ischemic stroke.

INTRODUCTION: Early prognosis prediction of acute ischemic stroke (AIS) can support clinicians in ch...

Opportunities and Challenges for Clinical Practice in Detecting Depression Using EEG and Machine Learning.

Major depressive disorder (MDD) is associated with substantial morbidity and mortality, yet its diag...

Comparative diagnostic accuracy of ChatGPT-4 and machine learning in differentiating spinal tuberculosis and spinal tumors.

BACKGROUND: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors...

Generation of high-resolution MPRAGE-like images from 3D head MRI localizer (AutoAlign Head) images using a deep learning-based model.

PURPOSE: Magnetization prepared rapid gradient echo (MPRAGE) is a useful three-dimensional (3D) T1-w...

A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases.

Neurological disorders, including cerebral vascular occlusions and strokes, present a major global h...

Integrative machine learning frameworks to uncover specific protein signature in neuroendocrine cervical carcinoma.

OBJECTIVE: Neuroendocrine cervical carcinoma (NECC) is a rare but highly aggressive tumor. The clini...

Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motor neurons. Al...

Hypothalamic atrophy in primary lateral sclerosis, assessed by convolutional neural network-based automatic segmentation.

Primary lateral sclerosis (PLS) is a motor neuron disease (MND) which mainly affects upper motor neu...

A novel machine learning based framework for developing composite digital biomarkers of disease progression.

BACKGROUND: Current methods of measuring disease progression of neurodegenerative disorders, includi...

A novel muscle network approach for objective assessment and profiling of bulbar involvement in ALS.

INTRODUCTION: As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement signi...

Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning.

INTRODUCTION: Excessive alcohol consumption negatively impacts physical and psychiatric health, life...

Risk prediction for elderly cognitive impairment by radiomic and morphological quantification analysis based on a cerebral MRA imaging cohort.

OBJECTIVE: To establish morphological and radiomic models for early prediction of cognitive impairme...

Machine Learning Approach for Sepsis Risk Assessment in Ischemic Stroke Patients.

BackgroundIschemic stroke is a critical neurological condition, with infection representing a signif...

A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.

Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present sig...

Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit.

INTRODUCTION: Delirium, frequently experienced by ischemic stroke patients, is one of the most commo...

A Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.

In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep...

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