Neurology

Segmentation of spinal rootlets across MRI contrasts with RootletSeg.

Segmentation of spinal nerve rootlets is relevant for spinal level estimation, lesion classification, neuromodulation therapy, and group-level analyses. The aim of this study was to develop a deep learning method for the automatic segmentation of C2-T1 dorsal and ventral spinal nerve rootlets on various MRI scans. The study included MRI scans from two open-access and one private dataset, consistin...

Cerebral blood flow alterations and machine learning classification in chronic low back pain using multi-PLD ASL radiomics.

Chronic low back pain (CLBP) is a prevalent condition with unclear pathophysiology and substantial socioeconomic burden. Cerebral blood flow (CBF) alterations have been implicated in CLBP, yet previous arterial spin labeling (ASL) studies using single post-labeling delay (PLD) have yielded inconsistent results. In this study, multi-PLD ASL was combined with machine learning to characterize CBF alt...

Disentangling amyloid polymorphs in normal aging and Alzheimer's disease using dual-probe spectral imaging.

Variability in Alzheimer's disease (AD) clinical presentation complicates mechanistic studies and therapeutic outcome prediction. Brain protein aggreg...

Effectiveness of Patent Foramen Ovale Occlusion Devices Versus Medical Therapy: An Indirect Comparison Based on Reconstructed Individual Patient Data.

BACKGROUND: Percutaneous closure of patent foramen ovale (PFO) reduces the risk of recurrent stroke in selected patients with cryptogenic stroke. Howe...

AI-Driven Hemodynamic Detection of Self-Induced Daydreaming With EMG-Based Physiological Triggers During Pre- and Post-Prandial States Using fNIRS and EGG.

BACKGROUND: Daydreaming can be monitored either to avoid it while doing hands-on tasks or to enhance it to foster creativity. Although significant res...

A Brainstem Radiomics Framework to Distinguish Progressive Supranuclear Palsy from Parkinson's Disease.

BACKGROUND: Differentiating progressive supranuclear palsy (PSP) from Parkinson's disease (PD) can be clinically challenging. In the neuroimaging fiel...

Integrative multi-omics analysis identifies coronin 1C as a potential mediator linking circadian rhythm disruption to neuroimmune dysregulation in ischemic stroke.

INTRODUCTION: Circadian rhythm disruption (CRD) is a major driver of immune dysregulation; however, whether CRD promotes ischemic stroke (IS) progress...

Developing machine learning-enhanced WHODAS 2.0 short forms for persons with dementia.

BACKGROUND: Disability assessment in dementia is important for care planning, but the full World Health Organization Disability Assessment Schedule 2....

Data-driven outcome prediction and rational resource allocation in severe traumatic brain injury: a multicenter retrospective study integrating survival analysis and machine learning ensembles.

INTRODUCTION: Severe traumatic brain injury (sTBI), is a leading cause of death and disability among young and middle-aged populations worldwide. OBJE...

Self-Supervised Contrastive Pre-Training for EEG-Based Recognition via Cross Device Representation Consistency.

Electroencephalography (EEG) has emerged as a powerful tool for modeling human brain states. However, the widespread adoption of EEG-based recognition...

SemSTNet: Medical EEG Semantic Metric Learning With Class Prototypes Generated by Pretrained Language Model.

Electroencephalography (EEG) feature learning is crucial for brain-machine interfaces and medical diagnostics. Existing deep learning models for class...

A 25-Year Bibliometric and Scientometric Analysis of Facial Paralysis Rehabilitation: Knowledge Structure, Influential Works, and Emerging Research Frontiers (2000-2025).

BACKGROUND: Facial paralysis rehabilitation has progressed substantially over the past two decades, yet the scientific landscape of this field remains...

[Staging of middle ear cholesteatoma surgery and its value for clinical decision-making].

Middle ear cholesteatoma is characterized by squamous epithelial accumulation within the middle ear cavity, which can lead to severe complications suc...

Explainable Artificial Intelligence to Predict Neurocognitive Disorder Progression in Multiple Sclerosis Using MRI and Clinical Data.

BACKGROUND: Cognitive impairment is common in multiple sclerosis (MS), yet the application of diagnostic frameworks of Neurocognitive Disorders (NCDs)...

Artificial Intelligence in Stroke Rehabilitation: A 20-Year Bibliometric Analysis of Digital Health Trends and Technologies.

BACKGROUND: Stroke remains a leading cause of long-term disability worldwide, and rehabilitation is essential for recovery. Although artificial intell...

GPND-AI NULISA: A 15-Protein AI classifier for diagnosis and co-pathology profiling across neurodegenerative diseases.

INTRODUCTION: Accurate clinical diagnosis of neurodegenerative diseases remains challenging, particularly when individuals have mixed pathologies. We ...

Bibliometric analysis of Alzheimer's and dementia research in Latin America.

INTRODUCTION: Dementia is increasing rapidly in Latin America and the Caribbean (LAC), but research output remains limited. Tracking publication trend...

Deep Learning-Based Augmented Contrast-Enhancement and Denoising for Reduced-Iodine and Low-Radiation 70-kVp Cerebral CT Angiography: A Prospective Study.

OBJECTIVE: To evaluate the feasibility of cerebral computed tomography angiography (CTA) obtained with reduced iodine and low radiation at 70 kVp and ...

Interpretable Machine Learning to Anticipate the Diagnostic Yield of EEG in the Emergency department. The EMINENCE study.

INTRODUCTION: Emergent electroencephalography (emEEG) is increasingly employed in the emergency department (ED) for evaluating altered consciousness a...

Deep learning for EEG-based sleep stage classification: a review.

Deep learning architectures are now widely applied in sleep electroencephalogram (EEG) analysis. These developments have significantly advanced EEG-ba...