Neurology

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

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Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors.

RATIONALE AND OBJECTIVES: To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from ...

Large Language Models in Neurology Research and Future Practice.

Recent advancements in generative artificial intelligence, particularly using large language models ...

Classification of Targets and Distractors in an Audiovisual Attention Task Based on Electroencephalography.

Within the broader context of improving interactions between artificial intelligence and humans, the...

Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factor...

An in-depth survey on Deep Learning-based Motor Imagery Electroencephalogram (EEG) classification.

Electroencephalogram (EEG)-based Brain-Computer Interfaces (BCIs) build a communication path between...

A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.

Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing p...

An EEG-based marker of functional connectivity: detection of major depressive disorder.

Major depressive disorder (MDD) is a prevalent psychiatric disorder globally. There are many assays ...

Mixed methods usability evaluation of an assistive wearable robotic hand orthosis for people with spinal cord injury.

BACKGROUND: Robotic hand orthoses (RHO) aim to provide grasp assistance for people with sensorimotor...

Neuroimaging to monitor worsening of multiple sclerosis: advances supported by the grant for multiple sclerosis innovation.

Key unmet needs in multiple sclerosis (MS) include detection of early pathology, disability worsenin...

Human Motor System-Based Biohybrid Robot-On-a-Chip for Drug Evaluation of Neurodegenerative Disease.

Biohybrid robots have been developed for biomedical applications and industrial robotics. However, t...

Frame-Level Teacher-Student Learning With Data Privacy for EEG Emotion Recognition.

Recently, electroencephalogram (EEG) emotion recognition has gradually attracted a lot of attention....

MuLHiTA: A Novel Multiclass Classification Framework With Multibranch LSTM and Hierarchical Temporal Attention for Early Detection of Mental Stress.

Mental stress is an increasingly common psychological issue leading to diseases such as depression, ...

Detecting and Tracking of Multiple Mice Using Part Proposal Networks.

The study of mouse social behaviors has been increasingly undertaken in neuroscience research. Howev...

A Bibliometric Analysis of Neuroscience Tools Use in Construction Health and Safety Management.

Despite longstanding traditional construction health and safety management (CHSM) methods, the const...

Neurophysiological mental fatigue assessment for developing user-centered Artificial Intelligence as a solution for autonomous driving.

The human factor plays a key role in the automotive field since most accidents are due to drivers' u...

Soft robotics-inspired sensing system for detecting downward movement and pistoning in prosthetic sockets: A proof-of-concept study.

BACKGROUND: Vertical displacement of the residual limb within transtibial prosthetic socket, often k...

A wearable system to assist impaired-neck patients: Design and evaluation.

Patients with neurological disorders, such as amyotrophic lateral sclerosis, Parkinson's disease, an...

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty.

In this study, we aim to develop a machine learning model to predict the level of coordination betwe...

A multimodal screening system for elderly neurological diseases based on deep learning.

In this paper, we propose a deep-learning-based algorithm for screening neurological diseases. We pr...

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