Neurology

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

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Machine Learning-Enhanced Cerebrospinal Fluid N-Glycome for the Diagnosis and Prognosis of Primary Central Nervous System Lymphoma.

The diagnosis and prognosis of Primary Central Nervous System Lymphoma (PCNSL) present significant c...

Alzheimer's Disease Dementia Prevalence in the United States: A County-Level Spatial Machine Learning Analysis.

A growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's ...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increas...

One for multiple: Physics-informed synthetic data boosts generalizable deep learning for fast MRI reconstruction.

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-f...

Intersecting impact of CAG repeat and huntingtin knockout in stem cell-derived cortical neurons.

Huntington's Disease (HD) is caused by a CAG repeat expansion in the gene encoding huntingtin (HTT)....

AI-assisted identification of disability patterns within identical EDSS grades.

BACKGROUND: The Neurostatus-Expanded Disability Status Scale (EDSS) is the most frequently used meas...

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients.

This study introduces a Brain-Computer Interface (BCI)-controlled upper limb assistive robot for pos...

AtlasGPT: a language model grounded in neurosurgery with domain-specific data and document retrieval.

OBJECTIVE: Large language models (LLMs) have shown promising performance on medical licensing examin...

Multiscroll hidden attractor in memristive autapse neuron model and its memristor-based scroll control and application in image encryption.

In current neurodynamic studies, memristor models using polynomial or multiple nested composite func...

Integrative neurorehabilitation using brain-computer interface: From motor function to mental health after stroke.

Stroke remains a leading cause of mortality and long-term disability worldwide, frequently resulting...

Transcriptomic analyses of human brains with Alzheimer's disease identified dysregulated epilepsy-causing genes.

BACKGROUND & OBJECTIVE: Alzheimer's Disease (AD) patients at multiple stages of disease progression ...

Feasibility of U-Net model for cerebral arteries segmentation with low-dose computed tomography angiographic images with pre-processing methods.

Subtraction computed tomography angiography (sCTA) can effectively separate enhanced cerebral arteri...

Using machine learning and electronic health record (EHR) data for the early prediction of Alzheimer's Disease and Related Dementias.

BACKGROUND: Over 6 million patients in the United States are affected by Alzheimer's Disease and Rel...

Deep learning-based EEG source imaging is robust under varying electrode configurations.

OBJECTIVES: Previous research has underscored the necessity of high-density EEG for accurate and rel...

Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.

Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing gene...

Diabetes: Non-Invasive Blood Glucose Monitoring Using Federated Learning with Biosensor Signals.

Diabetes is a growing global health concern, affecting millions and leading to severe complications ...

Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods.

Stroke is a serious cerebrovascular disease, and rehabilitation following the acute phase is particu...

A multimodal MRI-based machine learning framework for classifying cognitive impairment in cerebral small vessel disease.

The heterogeneity of cerebral small vessel disease (CSVD) with mild cognitive impairment (MCI) prese...

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