Neurology

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

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PhenoFlow: A Human-LLM Driven Visual Analytics System for Exploring Large and Complex Stroke Datasets.

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, th...

Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.

BACKGROUND: Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atro...

Machine learning based on event-related oscillations of working memory differentiates between preclinical Alzheimer's disease and normal aging.

OBJECTIVE: To apply machine learning approaches on EEG event-related oscillations (ERO) to discrimin...

Differentiating atypical parkinsonian syndromes with hyperbolic few-shot contrastive learning.

Differences in iron accumulation patterns have been observed in susceptibility-weighted images acros...

Identifying shared diagnostic genes and mechanisms in vascular dementia and Alzheimer's disease via bioinformatics and machine learning.

BACKGROUND: Alzheimer's disease (AD) and vascular dementia (VaD) share overlapping pathophysiologica...

Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model.

Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing,...

Neural correlates of empathy in donation decisions: Insights from EEG and machine learning.

Empathy is central to individual and societal well-being. Numerous studies have examined how trait o...

Generative modeling of the Circle of Willis using 3D-StyleGAN.

The circle of Willis (CoW) is a network of cerebral arteries with significant inter-individual anato...

Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomog...

Artificial Intelligence to Diagnose Complications of Diabetes.

Artificial intelligence (AI) is increasingly being used to diagnose complications of diabetes. Artif...

Deep learning-based denoising for unbiased analysis of morphology and stiffness in amyloid fibrils.

Understanding the morphology of amyloid fibrils is crucial for comprehending the aggregation and deg...

Establishing a machine learning dementia progression prediction model with multiple integrated data.

OBJECTIVE: Dementia is a significant medical and social issue in most developed countries. Practical...

Decoding Glioblastoma Heterogeneity: Neuroimaging Meets Machine Learning.

Recent advancements in neuroimaging and machine learning have significantly improved our ability to ...

A novel approach for brain connectivity using recurrent neural networks and integrated gradients.

Brain connectivity is an important tool for understanding the cognitive and perceptive neural mechan...

Automatic discrimination between neuroendocrine carcinomas and grade 3 neuroendocrine tumors by deep learning of H&E images.

Neuroendocrine neoplasms (NENs) arise from diffuse neuroendocrine cells and are categorized as eithe...

Learning a Hand Model From Dynamic Movements Using High-Density EMG and Convolutional Neural Networks.

OBJECTIVE: Surface electromyography (sEMG) can sense the motor commands transmitted to the muscles. ...

Parkinson's disease prediction using improved crayfish optimization based hybrid deep learning.

BackgroundPredicting the course of Parkinson's disease is essential for prompt diagnosis and treatme...

Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.

BACKGROUND AND OBJECTIVES: Early neuroprognostication in children with reduced consciousness after c...

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