Neurology

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

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Identification of differentially co-expressed genes with lipid metabolism in Parkinson's disease by bioinformatics analysis.

There was increasing evidence that lipid metabolism disorders played a significant part in the matur...

Systematic evaluation of AI-based text-to-image models for generating medical illustrations in neurosurgery: a multi-stage comparative study.

OBJECTIVE: This study evaluates the effectiveness of artificial intelligence (AI) models in generati...

Individualized structural network deviations predict surgical outcome in mesial temporal lobe epilepsy: a multicentre validation study.

BACKGROUND: Surgical resection is an effective treatment for medically refractory mesial temporal lo...

A dual path graph neural network framework for dementia diagnosis.

Dementia typically results from damage to neural pathways and the consequent degeneration of neurona...

A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geri...

Machine learning-assisted optimization of dietary intervention against dementia risk.

A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learni...

Multimodal nomogram integrating deep learning radiomics and hemodynamic parameters for early prediction of post-craniotomy intracranial hypertension.

To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing early intracrani...

Advanced multiscale machine learning for nerve conduction velocity analysis.

This paper presents an advanced machine learning (ML) framework for precise nerve conduction velocit...

Artificial Intelligence and Machine Learning in Neuromodulation for Epilepsy.

Recent advances in artificial intelligence (AI) and machine learning (ML) can revolutionize neuromod...

Design of a Mobile App and a Clinical Trial Management System for Cognitive Health and Dementia Risk Reduction: User-Centered Design Approach.

BACKGROUND: The rising prevalence of dementia is a major concern, with approximately 45% of cases li...

An EEG-based seizure prediction model encoding brain network temporal dynamics.

EEG-based seizure prediction enables timely treatment for patients, but its performance is limited b...

Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features.

Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital. ...

Scalable geometric learning with correlation-based functional brain networks.

Correlation matrices serve as fundamental representations of functional brain networks in neuroimagi...

A novel ST-GCN model based on homologous microstate for subject-independent seizure prediction.

Due to the lack of validated universal seizure markers, population-level prediction methods often ex...

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cogn...

Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.

Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy pr...

Advanced machine learning applications in fibromyalgia to assess the relationship between 3D spinal alignment with clinical outcomes.

This study leveraged machine learning (ML) models to explore the relationship between three-dimensio...

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.

Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT...

A multi-modal graph-based framework for Alzheimer's disease detection.

We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) ...

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