Neurology

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

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Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning.

BACKGROUND: Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide....

Population Health in Neurology and the Transformative Promise of Artificial Intelligence and Large Language Models.

This manuscript examines the expanding role of population health strategies in neurology, emphasizin...

Machine learning-based risk prediction model for neuropathic foot ulcers in patients with diabetic peripheral neuropathy.

BACKGROUND: Diabetic peripheral neuropathy (DPN) is a common chronic complication of diabetes, marke...

Effects of Robot-Assisted Therapy for Upper Limb Rehabilitation After Stroke: An Umbrella Review of Systematic Reviews.

BACKGROUND: Robotic rehabilitation, which provides a high-intensity, high-frequency therapy to impro...

Enhancing robotic skill acquisition with multimodal sensory data: A novel dataset for kitchen tasks.

The advent of large language models has transformed human-robot interaction by enabling robots to ex...

Unveiling CNS cell morphology with deep learning: A gateway to anti-inflammatory compound screening.

Deciphering the complex relationships between cellular morphology and phenotypic manifestations is c...

Neuromuscular Interfacing for Advancing Kinesthetic and Teleoperated Programming by Demonstration of Collaborative Robots.

This study addresses the challenges of Programming by Demonstration (PbD) in the context of collabor...

Transforming neurodegenerative disorder care with machine learning: Strategies and applications.

Neurodegenerative diseases (NDs), characterized by progressive neuronal degeneration and manifesting...

A liquid metal-based sticky conductor for wearable and real-time electromyogram monitoring with machine learning classification.

Skin electronics face challenges related to the interface between rigid and soft materials, resultin...

Cognitive load assessment through EEG: A dataset from arithmetic and Stroop tasks.

This study introduces a thoughtfully curated dataset comprising electroencephalogram (EEG) recording...

Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care.

OBJECTIVE: Dementia represents a growing public health challenge, affecting an increasing number of ...

A Machine Learning Prediction Model to Identify Individuals at Risk of 5-Year Incident Stroke Based on Retinal Imaging.

Stroke is a leading cause of death and disability in developed countries. We validated an AI-based p...

Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.

OBJECTIVE: The purpose of the current study is to explore the value of a nomogram that integrates cl...

EEG detection and recognition model for epilepsy based on dual attention mechanism.

In the field of clinical neurology, automated detection of epileptic seizures based on electroenceph...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and i...

Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study.

BACKGROUND: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contri...

Development and validation comparison of multiple models for perioperative neurocognitive disorders during hip arthroplasty.

This study aims to develop optimal predictive models for perioperative neurocognitive disorders (PND...

Detection of freely moving thoughts using SVM and EEG signals.

Freely moving thought is a type of thinking that shifts from one topic to another without any overar...

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