Neurology

IT: An interpretable transformer model for Alzheimer's disease prediction based on PET/MR images.

Alzheimer's disease (AD) represents a significant challenge due to its progressive neurodegenerative impact, particularly within an aging global demographic. This underscores the critical need for developing sophisticated diagnostic tools for its early detection and precise monitoring. Within this realm, PET/MR imaging stands out as a potent dual-modality approach that transforms sensor data into ...

AI-Driven Information for Relatives of Patients with Malignant Middle Cerebral Artery Infarction: A Preliminary Validation Study Using GPT-4o.

This study examines GPT-4o's ability to communicate effectively with relatives of patients undergoing decompressive hemicraniectomy (DHC) after malignant middle cerebral artery infarction (MMCAI). GPT-4o was asked 25 common questions from patients' relatives about DHC for MMCAI, twice over a 7-day interval. Responses were rated for accuracy, clarity, relevance, completeness, sourcing, and useful...

Exploring the perceptions and experiences of caregivers with the application of socially assistive robots in dementia care: A systematic review of qualitative studies.

BACKGROUND: The use of socially assistive robots in dementia care is growing, aimed at improving patient well-being and alleviating caregiver burden. ...

CLIC1 and IFITM2 expression in brain tissue correlates with cognitive impairment via immune dysregulation in sepsis and Alzheimer's disease.

BACKGROUND: Sepsis, a life-threatening condition driven by dysregulated host responses to infection, is associated with long-term cognitive impairment...

Artificial intelligence for the detection of interictal epileptiform discharges in EEG signals.

INTRODUCTION: Over the past decades, the integration of modern technologies - such as electronic health records, cloud computing, and artificial intel...

Exploring cortical excitability in children with cerebral palsy through lower limb robot training based on MI-BCI.

This study aims to compare brain activity differences under the motor imagery-brain-computer interface (MI-BCI), motor imagery (MI), and resting (REST...

Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images.

Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascula...

Evaluating the Effectiveness of Large Language Models in Providing Patient Education for Chinese Patients With Ocular Myasthenia Gravis: Mixed Methods Study.

BACKGROUND: Ocular myasthenia gravis (OMG) is a neuromuscular disorder primarily affecting the extraocular muscles, leading to ptosis and diplopia. Ef...

Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks.

Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encomp...

Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images.

BACKGROUND: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemor...

Voice biomarkers as prognostic indicators for Parkinson's disease using machine learning techniques.

Many people suffer from Parkinson's disease globally, a complicated neurological condition caused by the deficiency of dopamine, an organic chemical r...

Development and validation of Prediction models for radiation-induced hypoglossal neuropathy in patients with nasopharyngeal carcinoma.

BACKGROUND AND PURPOSE: To establish predictive models for radiation-induced hypoglossal neuropathy (RIHN) in patients with nasopharyngeal carcinoma (...

Advances in MRI optic nerve segmentation.

Understanding optic nerve structure and monitoring changes within it can provide insights into neurodegenerative diseases like multiple sclerosis, in ...

Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential gene...

A novel intelligent grade classification architecture for Patent Foramen Ovale by Contrast Transthoracic Echocardiography based on deep learning.

Patent foramen ovale (PFO) is one of the main causes of ischemic stroke. Due to the complex characteristics of contrast transthoracic echocardiography...

The Cognivue Amyloid Risk Measure (CARM): A Novel Method to Predict the Presence of Amyloid with Cognivue Clarity.

INTRODUCTION: At the present time, clinical detection of individuals who have amyloid in their brain is not possible without expensive biomarkers. The...

DP-MP: a novel cross-subject fatigue detection framework with DANN-based prototypical representation and mix-up pairwise learning.

. Electroencephalography (EEG) is widely recognized as an effective method for detecting fatigue. However, practical applications of EEG for fatigue d...

Prediction of new-onset migraine using clinical-genotypic data from the HUNT Study: a machine learning analysis.

BACKGROUND: Migraine is associated with a range of symptoms and comorbid disorders and has a strong genetic basis, but the currently identified risk l...

Ensemble deep learning for Alzheimer's disease diagnosis using MRI: Integrating features from VGG16, MobileNet, and InceptionResNetV2 models.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain...