Neurology

Low-field strength MRI (0.55T) for stereotactic and functional neurosurgery using deep learning-based reconstruction algorithm: Preliminary experiences.

BACKGROUND: There has been a trend toward exclusive use of high MRI field strength (1.5 T or above) for stereotactic neurosurgery imaging. However, low field strength (0.55 T) MRI may have advantages related to availability, cost, image distortion, and artifact. Low field strength MRI recently has been shown to be effective for diagnosis of brain tumor and stroke, but the benefits and limitations ...

An exploratory study of headache pain intensity using facial expressions and APEX frames.

In this cross-sectional observational study, we developed a computer vision-based machine-learning model to estimate headache pain intensity from facial action units (AUs) and evaluated its association with self-reported pain. Facial videos were obtained from 80 adults aged 19-80 years receiving headache care at a single institution (outpatient, inpatient, or emergency; IRB No. 2024-08-003). A mul...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

MultiMindNet: AI-based mental health analysis using hybrid deep learning approach and Hybrid Ant-Grey Wolf Optimization (HAGWO) algorithm.

Mental health disorders like depression and anxiety pose global challenges, requiring accurate, non-invasive detection methods. Classical modes of dia...

Excellent agreement between automated deep learning-based and manual diffusion-weighted imaging infarct volume measurements in hyperacute stroke.

BACKGROUND: Diffusion-weighted imaging (DWI) infarct volume and infarct growth are important imaging biomarkers in patients with acute ischemic stroke...

Geo-Mamba: Geometry-informed state-space learning of functional brain organization.

Functional magnetic resonance imaging (fMRI) derived functional connectivity (FC) is represented as graphs and as correlation or covariance matrices t...

Foundation models for brain imaging: A systematic review.

Foundation models (FMs), large neural networks pretrained on extensive and diverse datasets, have revolutionized artificial intelligence and demonstra...

Discovery of a novel TFEB activator targeting lysosomal dysfunction in amyotrophic lateral sclerosis using artificial intelligence-based virtual screening.

Lysosomal dysfunction is a defining feature of multiple neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), yet effective pharm...

Free water in the hippocampal cingulum as a Radiomic biomarker for Identifying inflammatory neuropsychiatric Lupus: A cross-sectional case-control study.

PURPOSE: Neuropsychiatric systemic lupus erythematosus (NPSLE) is a serious manifestation of systemic lupus erythematosus (SLE), yet its neuroimaging ...

Classification of fallers in Parkinson's disease through machine learning based feature analysis.

Classification of fallers in Parkinson's disease (PD) is challenging due to the heterogenous motor and non-motor symptoms. We developed a machine lear...

Quantitative Assessment of Third Molar Extraction Difficulty and Nerve Injury Risk Using Artificial Intelligence and Image Processing.

PURPOSE: Tooth pattern and surrounding tissue can affect the complexity of third molar (3M) extraction surgery. This study aimed to develop a new, mor...

Chemometric and learning-based multivariate models for quantifying a challenging quaternary mixture of bupropion, dextromethorphan, and their related impurities by UV-Vis spectrophotometry.

This study presents a robust, green, sustainable and time-efficient approach for the simultaneous determination of Bupropion HCl (BUP) and Dextrometho...

Neuromodulation of the central nervous system for facial pain.

Chronic craniofacial pain can lead to significant morbidity and reduced quality-of-life. Refractory pain subsequently leads to maladaptive changes wit...

Imaging and genomics in stroke.

Imaging after ischemic and hemorrhagic stroke may allow measurement of key phenotypes of injury and recovery for which targeted therapies are still la...

Wheelchair movement signal classification from EEG for motor-impaired individuals using novel deep learning architecture.

Purpose: Traditional wheelchair controls often limit independence and pose safety risks for motor-impaired users. To address these challenges, this st...

Pathology-Anchored Transcranial Sonography: A Cascaded Super-Resolution Deep Learning System for Early-Stage Parkinson's Disease Grading.

Delayed diagnosis of Parkinson's disease (PD) due to undetectable early pathological changes remains a major clinical challenge limiting effective tre...

Artificial Intelligence and Machine Learning in Pediatric Epilepsy: A Systematic Review.

INTRODUCTION: To evaluate the progress of artificial intelligence (AI)-based tools in interpreting clinical data, to compare the existing models, to i...

A Few-Layer Multilayer Perceptron is Worth Attention for EEG Classification in Rapid Serial Visual Presentation Task.

Rapid serial visual presentation (RSVP) enables efficient electroencephalography (EEG)-based brain-computer interfaces, yet single-trial decoding rema...

Neuroplasticity after Stroke: Adaptive and Maladaptive Mechanisms in Evidence-Based Rehabilitation.

BACKGROUND AND PURPOSE: Ischemic stroke remains a leading cause of long-term disability and persistent motor deficits. While neuroplasticity drives po...

Severity-dependent alterations of EEG microstate dynamics in obsessive-compulsive disorder.

BACKGROUND: While elevated symptom severity in obsessive-compulsive disorder (OCD) is associated with a profound clinical burden and escalating psychi...