Neurology

DoC-Informer: Automated Discrimination of Disorders of Consciousness under Adaptive EEG Settings.

OBJECTIVE: Disorders of consciousness (DoC) diagnosis critically depends on accurate state discrimination to guide treatment and prognosis. Current EEG-based techniques face challenges of incomplete electrode coverage and manual feature reliance, due to the complex nature of DoC conditions. METHODS: This study proposes DoC-Informer, a CNN-Transformer framework for automated DoC discrimination unde...

Impact of Imaging Acquisition and Protocol Variability on Artificial Intelligence Model Performance: A Secondary Analysis of the ASFNR Artificial Intelligence Competition.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) models have shown promise in neuroradiology, yet their real-world generalizability remains uncertain, partly due to variability in imaging acquisition and protocols. We aimed to evaluate the impact of data source, scanner manufacturer, scan mode, slice thickness, and the AI models developed by participating teams on AI performance in this second...

Machine learning-based identification of potential diagnostic signatures in spinal cord injury.

STUDY DESIGN: Bioinformatics analysis. OBJECTIVES: To explore the diagnostic signatures for patients with spinal cord injury (SCI). SETTING: Shenyang,...

Deep learning for fast screening and localization of spinal dural arteriovenous fistulas to enhance clinical workflow.

Manual processing of spinal CTA images for detecting dural arteriovenous fistulas (SDAVF) is laborious and operator-dependent. We developed an automat...

Machine learning-guided Huanglian Jiedu decoction targets STING in periodontitis-induced Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder increasingly associated with peripheral inflammatory conditions such as chronic p...

Structural signature of plasma proteins classifies the status of Alzheimer's disease.

Alzheimer's disease (AD) involves proteostasis dysregulation causing protein misfolding, but whether these structural changes manifest as plasma confo...

A Preliminary Report: Screening Idiopathic Scoliosis (IS) With a Smartphone Application.

INTRODUCTION: Idiopathic scoliosis (IS) is a 3-dimensional spinal deformity that affects ∼3.1% of the population. Current strategies for IS screening ...

Explainable AI for gait speed analysis from multimodal data fusion.

Gait speed analysis is vital for applications in healthcare, rehabilitation, human-robot interaction, and autonomous systems, necessitating robust met...

ATCRN: Attention-guided Temporal Convolutional Remix Network for P300 speller.

BACKGROUND: The P300 speller is a prominent brain-computer interface (BCI) that facilitates communication by detecting P300 event-related potentials. ...

A feasibility open-label clinical trial utilizing second-generation artificial intelligence based on the constrained-disorder principle in patients with Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder treated with Levodopa, but long-term use often causes motor complications like "w...

Neuroimaging-Based Deep Learning Applications for Lesion Detection and Predicting the Outcome Following Epilepsy Surgery.

Neuroimaging studies are essential for evaluating patients with drug-resistant focal epilepsy and determining their candidacy for epilepsy surgery. Th...

[CefaleApp: A Mobile Application for Migraine Diagnosis in Primary Health Care].

INTRODUCTION: Headaches are the main reason for visits to Neurology clinics, and migraine is the most common primary headache. with migraine being the...

Integrating Multimodal Neuroimaging and Physical-Health Markers for Autism Spectrum Disorder in the ABCD Study.

BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by diverse presentations, which complicates the ide...

Task-evoked brain network dynamics underlying cognitive control using source-localized EEG and machine learning.

OBJECTIVE: Functional connectivity (FC) coordinates brain activity during cognitive tasks, yet the influence of demographic variables and health facto...

Multimodal Representation Learning for Parsing Biological Heterogeneity in Psychiatric Neuroimaging.

For decades, psychiatric neuroimaging has searched for biomarkers of depression and other disorders, but they remain elusive in clinical practice. Whi...

Early Diagnosis of Alzheimer's: Machine Learning Analysis Leveraging Structural MRI.

INTRODUCTION: Alzheimer's disease (AD) is characterized by significant brain atrophy, detectable via structural MRI. There has been less focus on cort...

Exploring key risk factors for loss to follow-up after hospitalization for acute stroke.

BACKGROUND: A significant proportion of stroke patients are lost to follow-up (LTFU) after discharge, which may increase risks of morbidity, mortality...

Right and Left Atrial Dysfunction as Independent Cardiovascular Risk Factors: A UK Biobank Study.

BACKGROUND: Atrial cardiopathy often precedes atrial fibrillation (AF) and has emerged as an independent risk factor for cardiovascular outcomes. Howe...

NeuroMDAVIS: Visualization of Single-Cell Multi-Omics Data under Deep Learning Framework.

Single-cell technologies have favored extensive advancements in cell-type discovery, cell state identification, development of lineage tracing, and di...

EEG microstate-derived dynamic network biomarkers for lateralization and structural etiology in temporal lobe epilepsy.

OBJECTIVE: Temporal lobe epilepsy (TLE) is the most common focal epilepsy but remains highly heterogeneous across hemispheric and structural etiology....