Neurology

DeepVBM: A fully automatic and efficient voxel-based morphometry via deep learning-based segmentation and registration methods.

Voxel-based morphometry (VBM) using T1-weighted magnetic resonance imaging is a pivotal tool for ass...

Uncertainty in deep learning for EEG under dataset shifts.

As artificial intelligence (AI) is increasingly integrated into medical diagnostics, it is essential...

A machine learning model for prediction of risk of dementia superimposed on delirium in intensive care patients.

BACKGROUND: The objective of this study was to construct a predictive model using multiple machine l...

Enhancing upper limb motor recovery prediction after acute stroke using EEG and subacute data.

Electroencephalography (EEG) has shown promise in assessing and monitoring functional recovery in st...

Artificial Intelligence-based detection of neuropsychiatric lupus: an exploratory meta-analysis of neuroimaging and multimodal biomarker models.

Neuropsychiatric systemic lupus erythematosus (NPSLE) remains challenging to diagnose because of het...

Toward early warning of unsafe behavior of excavator operators under time pressure: experimental evidence and EEG-based detection via RCF-IncepLite model.

Time pressure can impair the cognitive functioning of excavator operators, thereby increasing unsafe...

MRI neuroimaging-based Alzheimer's disease stage classification using deep neural network with convolutional block attention module and GAN-style noise injection.

Millions of individuals worldwide suffer from Alzheimer's disease (AD), a chronic, incurable neurolo...

Amyloid-β and phosphorylated-tau accumulation in retrosplenial cortex of young Alzheimer's disease model: Impact on network activity.

BackgroundThe retrosplenial cortex (RSC) is a cortical area that functions as a key component of the...

Diagnostic Accuracy of Cerebrospinal Fluid Presepsin vs. Procalcitonin in Post-Neurosurgical Bacterial Ventriculitis/Meningitis: A Machine Learning Analytical Approach.

This study aimed to evaluate the diagnostic accuracy of cerebrospinal fluid presepsin and procalcito...

Simplifying Depression Diagnosis: Single-Channel EEG and Deep Learning Approaches.

Major depressive disorder (MDD) or depression is a chronic mental illness that significantly impacts...

Developing a Multimodal Screening Algorithm for Mild Cognitive Impairment and Early Dementia in Home Health Care: Protocol for a Cross-Sectional Case-Control Study Using Speech Analysis, Large Language Models, and Electronic Health Records.

BACKGROUND: Mild cognitive impairment and early dementia (MCI-ED) are frequently unrecognized in rou...

The Accuracy of Artificial Intelligence Models in Carpal Tunnel Diagnosis: A Systematic Review and Meta-analysis.

BACKGROUND: Artificial intelligence (AI) has been integrated into diagnostic modalities like nerve c...

Machine Learning Approach to Predict Pain Outcomes Following Primary and Secondary Targeted Muscle Reinnervation in Amputees.

INTRODUCTION: Targeted Muscle Reinnervation (TMR) can prevent and treat neuropathic pain in amputees...

Multimodal fusion and explainability of artificial intelligence models in Alzheimer's Disease detection.

The integration of multimodal data has emerged as a powerful strategy for enhancing the accuracy and...

Explainable multi-modal approach for uncovering key predictors of stroke-risk from ECG, EMG, blood pressure, and respiratory signals.

Accurate and timely stroke-risk prediction is necessary to help patients at risk take guided measure...

Rethinking Cardiopulmonary Bypass Management in The Digital Health Era.

Minimally invasive and robotic cardiac surgery have been developed to reduce surgical trauma, shorte...

Multi-dimensional EEG analysis reveals distinct neurophysiological patterns in Alzheimer's and frontotemporal dementia.

AI-driven framework for accurate detection of Alzheimer's disease in EEG.

Alzheimer's Disease (AD) is a rapidly growing neurodegenerative disorder that severely impairs cogni...

Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.

Artificial intelligence and neuroimaging enable accurate dementia prediction but often involve 'blac...

FedEMG: Achieving Generalization, Personalization, and Resource Efficiency in EMG-Based Upper-Limb Rehabilitation Through Federated Prototype Learning.

Upper extremity amputation, often necessitated by traumatic injuries, significantly impacts an indiv...

AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image- and Time-Series-Based Approaches.

OBJECTIVE: Manually distinguishing between seizure and non-seizure events in intracranial electroenc...