Neurology

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

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Adapting to evolving MRI data: A transfer learning approach for Alzheimer's disease prediction.

Integrating 3D magnetic resonance imaging (MRI) with machine learning has shown promising results in...

Parallel convolutional neural network and empirical mode decomposition for high accuracy in motor imagery EEG signal classification.

In recent years, the utilization of motor imagery (MI) signals derived from electroencephalography (...

Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling.

Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognit...

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.

Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. T...

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease.

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly app...

Automated analysis of spoken language differentiates multiple system atrophy from Parkinson's disease.

BACKGROUND AND OBJECTIVES: Patients with synucleinopathies such as multiple system atrophy (MSA) and...

Patch-Wise Deep Learning Method for Intracranial Stenosis and Aneurysm Detection-the Tromsø Study.

Intracranial atherosclerotic stenosis (ICAS) and intracranial aneurysms are prevalent conditions in ...

Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.

BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute...

Supervised Contrastive Learning-Based Domain Generalization Network for Cross-Subject Motor Decoding.

Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding sy...

The global research of magnetic resonance imaging in Alzheimer's disease: a bibliometric analysis from 2004 to 2023.

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using ...

Predicting delayed neurological sequelae in patients with carbon monoxide poisoning using machine learning models.

INTRODUCTION: Delayed neurological sequelae is a common complication following carbon monoxide poiso...

Shape Anisotropy-Dependent Leaking in Magnetic Neurons for Bio-Mimetic Neuromorphic Computing.

Spiking neural networks seek to emulate biological computation through interconnected artificial neu...

Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis.

The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data stru...

A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain-Computer Interfaces to Enhance Motor Imagery Classification.

Enhancing motor disability assessment and its imagery classification is a significant concern in con...

DML-MFCM: A multimodal fine-grained classification model based on deep metric learning for Alzheimer's disease diagnosis.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder. There are no drugs and methods...

Current state and promise of user-centered design to harness explainable AI in clinical decision-support systems for patients with CNS tumors.

In neuro-oncology, MR imaging is crucial for obtaining detailed brain images to identify neoplasms, ...

AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders.

INTRODUCTION: Evaluating neurocognitive functions and diagnosing psychiatric disorders in older adul...

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