Neurology

Associations of body mass index and metabolic health with stroke risk in a large prospective cohort with time updated covariates.

The global prevalence of overweight and obesity is rising, and recent studies have established an independent contribution of adiposity to stroke risk. How the increased risk associated with adiposity relates to other factors including metabolic health is not fully understood. We analyzed 132,045 participants from the Northern Sweden Health and Disease Study with repeated health examinations (1985...

Predicting progression-free survival in glioblastoma with neuroimaging and machine learning.

PURPOSE: Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prior to medical intervention could strengthen clinical decision-making and improve patient care. Here, we utilize machine learning (ML) to predict PFS in GBM patients using resting state network (RSN) connectivity before medical intervention. METHODS: G...

Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies.

BACKGROUND: Artificial intelligence (AI) has rapidly emerged within healthcare systems and neurological rehabilitation with the potential to revolutio...

A multi-sequence MRI integration framework using SwinUNETR-v2 for multiple sclerosis lesion segmentation.

Multiple Sclerosis (MS) is a chronic brain disease that affects the brain and spinal cord, where Magnetic Resonance Imaging (MRI) plays a key role in ...

Predictors of warfarin therapeutic dose across thromboembolic indications: an exploratory early clinical evaluation study using machine learning-integrated explainable artificial intelligence and Bayesian models.

BACKGROUND: Warfarin dosing varies widely due to genetic, demographic, and clinical factors, but it is unknown whether the importance, equilibrium and...

The positive predictive value of ICD-10-AM S06.0~ concussion codes for mild traumatic brain injury.

BACKGROUND: Increasing concern regarding long-term consequences of mild traumatic brain injury (mTBI; concussion) highlights the need for an accurate ...

From Three to Eight Clusters: A Machine Learning-Based Classification of Operated Adult Spinal Deformity Patients.

BACKGROUND: Adult spinal deformity (ASD) is a heterogeneous condition encompassing diverse etiologies, clinical presentations, and surgical challenges...

Turning Compliance Into Action: An Augmented Intelligence-Enabled Framework for Advocacy Organizations to Drive Population Health Improvement.

Community Health Needs Assessments (CHNAs), mandated by the Affordable Care Act for tax-exempt hospitals, represent an underutilized yet rich data sou...

Idiopathic Intracranial Hypertension Is Characterized by a Distinct Proteomic Profile.

OBJECTIVES: The pathophysiology of idiopathic intracranial hypertension (IIH) is poorly understood and disease-specific biomarkers are lacking. We aim...

Age-Dependent z Scores and eGFR-Adjusted Reference Ranges for Neurofilament Light: A Practical Approach for Clinical Laboratories.

BACKGROUND: Neurofilament light (NfL) has emerged as a sensitive biomarker of neuroaxonal damage across a wide spectrum of neurological conditions, in...

Clinical evaluation of accelerated breath-held and free-breathing cine cardiac MRI using model-based deep learning reconstruction (SonicDL) in children and young adults.

BACKGROUND: Balanced steady-state free-precession (bSSFP) cine imaging is the clinical standard for ventricular function assessment but requires multi...

Tree-based and sparse logistic models for predicting one-month postoperative performance status after surgery for spinal metastases.

PURPOSE: We aimed to develop and internally validate prediction models for one-month postoperative performance status (PS) after surgery for spinal me...

Machine learning-driven drug repurposing for GPR17: activity prediction via graph neural networks and multistage computational validation.

To identify novel GPR17-targeting ligands with potential relevance to multiple sclerosis (MS) therapy, we developed an integrated computational workfl...

Predicting the antifungal effects of ethanolic propolis and geopropolis extracts on Aspergillus flavus growth using mechanistic and machine-learning models.

This study evaluated the antifungal potential of ethanolic propolis (EEP) and geopropolis (EEG) extracts against Aspergillus flavus, integrating mecha...

Frequency- and Network-Specific Changes in Functional Connectivity Reflect Pathophysiological Mechanisms across Parkinson's Disease Stages.

OBJECTIVE: Parkinson's disease (PD) is increasingly conceptualized as a disorder of large-scale brain networks, yet whether and how frequency-specific...

The emerging role of oligodendrocytes in Alzheimer's disease: Integrating bibliometric insights with molecular pathogenesis.

BackgroundOligodendrocytes (OLs) have received relatively limited attention in Alzheimer's disease (AD) research; however, recent studies highlight th...

Peripheral immune and vascular inflammatory signatures precede Alzheimer's disease onset: A serum proteomic study.

BackgroundNeuroinflammation plays an important role in the pathogenesis of Alzheimer's disease, but systemic immune alterations preceding clinical ons...

Comparative machine learning for accurate EEG-based epileptic seizure state classification using sub-band analysis.

Timely identification of seizure-related EEG states can support clinical assessment and motivate future monitoring tools. This study investigates a co...

Fully Biobased, Robust, and High-Conductivity Hydrogel for High-Fidelity Electrophysiological Monitoring and Deep Learning-Assisted Stroke Rehabilitation.

Developing sustainable bioelectronics that simultaneously integrate mechanical robustness, high conductivity, biocompatibility, and system-level funct...

Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data.

Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction...