Neurology

Detection of Pilots' Psychological Workload during Turning Phases Using EEG Characteristics.

Pilot behavior is crucial for aviation safety. This study aims to investigate the EEG characteristics of pilots, refine training assessment methodologies, and bolster flight safety measures. The collected EEG signals underwent initial preprocessing. The EEG characteristic analysis was performed during left and right turns, involving the calculation of the energy ratio of beta waves and Shannon ent...

Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial.

BACKGROUND: In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by...

MFCC-CNN: A patient-independent seizure prediction model.

BACKGROUND: Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) sig...

A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning.

Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main d...

MCAS-GP: Deep Learning-Empowered Middle Cerebral Artery Segmentation and Gate Proposition.

With the fast development of AI technologies, deep learning is widely applied for biomedical data analytics and digital healthcare. However, there rem...

Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.

To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical s...

Contrastive fine-grained domain adaptation network for EEG-based vigilance estimation.

Vigilance state is crucial for the effective performance of users in brain-computer interface (BCI) systems. Most vigilance estimation methods rely on...

Significance of plasma p-tau217 in predicting long-term dementia risk in older community residents: Insights from machine learning approaches.

INTRODUCTION: Whether plasma biomarkers play roles in predicting incident dementia among the general population is worth exploring.

BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.

The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical ...

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.

BACKGROUND: With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and ...

A review: artificial intelligence in image-guided spinal surgery.

INTRODUCTION: Due to the complex anatomy of the spine and the intricate surgical procedures involved, spinal surgery demands a high level of technical...

Convolutional neural networks can identify brain interactions involved in decoding spatial auditory attention.

Human listeners have the ability to direct their attention to a single speaker in a multi-talker environment. The neural correlates of selective atten...

An efficient channel recurrent Criss-cross attention network for epileptic seizure prediction.

Epilepsy is a chronic disease caused by repeated abnormal discharge of neurons in the brain. Accurately predicting the onset of epilepsy can effective...

Ensemble Deep Random Vector Functional Link Network Using Privileged Information for Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) is a progressive brain disorder. Machine learning models have been proposed for the diagnosis of AD at early stage. Recently,...

A Multi-Classification Accessment Framework for Reproducible Evaluation of Multimodal Learning in Alzheimer's Disease.

Multimodal learning is widely used in automated early diagnosis of Alzheimer's disease. However, the current studies are based on an assumption that d...

Graph Embedded Ensemble Deep Randomized Network for Diagnosis of Alzheimer's Disease.

Randomized shallow/deep neural networks with closed form solution avoid the shortcomings that exist in the back propagation (BP) based trained neural ...

A Cascaded Mutliresolution Ensemble Deep Learning Framework for Large Scale Alzheimer's Disease Detection Using Brain MRIs.

Alzheimer's is progressive and irreversible type of dementia, which causes degeneration and death of cells and their connections in the brain. AD wors...

Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.

Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics a...

Single-Task and Dual-Task Gait Performance After Sport-Related Concussion: A Machine Learning Statistical Approach.

BACKGROUND: This study evaluated 2 different dual-task (DT) conditions during tandem gait (TG) to predict sport-related concussion (SRC) diagnosis.

Accuracy of thoracic nerves recognition for surgical support system using artificial intelligence.

We developed a surgical support system that visualises important microanatomies using artificial intelligence (AI). This study evaluated its accuracy ...