Neurology

Diabetes-linked metabolic dysfunction relates with distinct tau phosphorylation patterns, neuroinflammation and cognitive impairment in mouse models of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques and tau neurofibrillary tangles, with tau pathology being closely linked to cognitive decline. Growing evidence suggests that metabolic dysfunction including type 1 diabetes (T1D) and type 2 diabetes (T2D), as well as prediabetes (PreDM), exacerbate AD by promoting different degrees of insulinop...

A Causal Validation augmented Temporal Convolutional Framework for Brain Effective Connectivity Networks Estimation.

Advancements in neuroimaging have facilitated unprecedented insights into brain connectivity, making the study of brain effective connectivity networks (ECNs) essential for understanding neurological functions and diseases. Recently, neural networks (NNs) have emerged as powerful tools for ECN estimation due to their prominent universal approximation ability and less reliance on prior knowledge. H...

QENNA: A quantum-enhanced neural network for early Alzheimer's detection using magnetic resonance imaging.

Early detection of Alzheimer's disease (AD) is essential for effective clinical intervention and disease management. However, conventional Deep Learni...

GCANet: Enhancing EEG-based auditory attention decoding with temporal frequency GCN and cross attention mechanisms.

In complex auditory environments, individuals rely on selective auditory attention to focus on a target speaker while suppressing competing sounds, a ...

Development and validation of a novel orthopedic blood use prediction model incorporating lab tests and thromboelastography.

OBJECTIVE: To develop and validate a prediction model integrating laboratory parameters and thromboelastography (TEG) for forecasting blood transfusio...

Electromyography Signal Classification With Artificial Intelligence for Detection of Neuromuscular Disorders Using a Large Clinically-Acquired Database.

INTRODUCTION/AIMS: Artificial intelligence (AI) has shown potential in analyzing electromyography (EMG) signals, but clinical applicability remains li...

Emerging risk factors for stroke and bleeding in patients with atrial fibrillation and heart failure-a narrative review.

Atrial fibrillation (AF) and heart failure (HF) frequently coexist, which leads to adverse clinical outcomes and a significant increase in the risk of...

Spectral anomaly detection in physiological time-series data: A systematic review.

BACKGROUND: The identification of anomalies in physiological time-series data, specifically ECG and EEG spectra, is a key part of the diagnostic proce...

Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.

INTRODUCTION: In the Movement Disorder Society criteria for the diagnosis of Parkinson's disease (PD), evaluation of the presynaptic dopamine system s...

Delineating white matter phenotypes of sensori-/psychomotor functioning in large-scale cohorts of healthy individuals and patients with mental disorders across the lifespan (whiteSPAN): rationale and methods of an interdisciplinary bicentric project.

Aberrant sensori-/psychomotor functioning-including muscular hand weakness, sedentary behavior, psychomotor agitation, slowing, agitation, apathy, and...

Artificial Intelligence Technologies Supporting Nurses' Clinical Decision-Making: A Systematic Review.

BACKGROUND: The use of technology to support nurses' decision-making is increasing in response to growing healthcare demands. AI, a global trend, hold...

Hermite-type neural network operators: derivative-informed frameworks for functional neuroimaging and signal processing.

This paper introduces Hermite-type Neural Network (HNN) operators and their integral-based extension, Hermite-Kantorovich Neural Network (HKNN) operat...

Sleep stage classification from ECG using machine learning: Evaluating the impact of signal duration.

Sleep stage flagging is critical for diagnosing conditions like insomnia, sleep apnea, and narcolepsy. Traditional methods rely on time-intensive manu...

Preliminary findings on a deep learning model using electroencephalogram for multi-level neuropathic pain detection in post-stroke patients.

AIM: Neuropathic pain occurs commonly after stroke and represents a major source of disability for affected patients. This study aims to develop an ac...

Development and validation of a machine learning model to predict 30-day mortality in ischemic stroke patients with consciousness impairment: Insights from MIMIC-IV database and multicenter ICU data in China.

BACKGROUND: Patients with ischemic stroke complicated by consciousness disorders remain associated with high mortality risks. This study aims to devel...

Mortality prediction among older people in primary care: a comparison of predictive performance of the frailty index with regression-based and machine learning models.

INTRODUCTION: The frailty index is widely used to identify vulnerable individuals at risk of adverse outcomes like mortality. However, its predictive ...

Imaging of vestibular function and disorders and its clinical relevance.

PURPOSE OF REVIEW: To discuss recent advances in imaging of the structural organization and functional connectivity of central vestibular disorders wi...

Deep learning-based image reconstruction significantly improves image quality of MRI examinations of the orbit at 3 Tesla.

PURPOSE: The purpose of this study was to assess the benefit of a deep learning-based image reconstruction (DLBIR) for improving image quality in orbi...

Predicting Pharmacological Treatment Response in Migraine Using AI/ML: A Scoping Review of the Evidence and Future Directions.

The treatment of migraine is hampered by inter-individual variability, leading to an inefficient "trial and error" approach. Artificial intelligence (...

Clinically oriented deep learning framework for automated vessel wall segmentation in black-blood MRI: a multi-center study.

OBJECTIVES: To develop and validate a clinically applicable deep learning framework for automated segmentation of intracranial and carotid vessel wall...