Neurology

CT-based radiomic markers to predict late-onset seizures after traumatic brain injury.

Post-traumatic epilepsy (PTE) is a major long-term complication of traumatic brain injury (TBI), but early risk prediction remains imprecise. Radiomics enables quantitative analysis of subtle abnormalities on non-contrast head CT (NCCT) that are not readily visible on routine imaging and may improve early risk stratification. This pilot study assessed the performance of radiomic features from acut...

Robot-assisted lumbar facet joint infiltration improves accuracy and reduces radiation exposure compared to the manual technique in a comparative phantom study.

Compact robotic systems offer new opportunities for spinal procedures outside the operating room, but their potential for small-scale interventions such as facet joint infiltrations remains underexplored. The objective of this study was to assess the feasibility, accuracy, radiation exposure, and user experience of a compact robotic assistance system (Micromate) for lumbar facet joint infiltration...

NerveAI- a machine learning algorithm for detection of nerve pain in the head and neck.

Nerve pain screening in patients with headache disorders requires specialized clinical knowledge that is frequently not readily accessible at the init...

Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers.

BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising no...

NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection.

To address the inherent complexity and nonlinearity of electroencephalogram (EEG) signals, this study proposes a refined classification framework, Neu...

Decomposing heterogeneity in disease progression speeds and pathways.

Understanding why patients with the same diagnosis exhibit markedly different disease progression-some rapidly, others slowly, with distinct symptom p...

Alterations in resting-state neural networks in subacute non-fluent aphasia after stroke: a fNIRS study.

BACKGROUND: Post-stroke aphasia is a prevalent and often disabling language impairment. Despite its high incidence, the neuropathological mechanisms u...

WBT-DC pipeline: a cross-cohort and cross-platform disease classification pipeline based on whole-blood transcriptomics.

BACKGROUND: Machine-learning models based on tissue transcriptomic data are powerful tools for disease classification. However, their clinical adoptio...

Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.

This study introduces a novel adaptive deep learning framework for EEG-based schizophrenia diagnosis that addresses the limitations of existing static...

Machine Learning Prediction for 6-Month Outcomes in Traumatic Cerebral Venous Sinus Thrombosis.

This study developed and externally validated a multicenter machine learning framework to predict 6-month poor functional outcome (modified Rankin Sca...

DeepEpiX: a software for visualization, annotation and automatic epileptical spike detection in MEG recordings.

BACKGROUND: Magnetoencephalography (MEG) non-invasively records brain activity. It is widely used in presurgical evaluation of drug-resistant epilepsy...

Decoding Parkinson's progression: A multi-modal SuStaIn ensemble approach validated on real-world PPMI data.

BACKGROUND: Parkinson's disease (PD) exhibits substantial heterogeneity in clinical presentation and longitudinal progression, complicating prognosis,...

Development of an Interpretable Deep Learning-Based Segmentation Algorithm for Automated Assessment of Oral Diadochokinesis in Progressive Neurological Diseases.

PURPOSE: We aimed to develop a universal, fully automated segmentation algorithm that allows robust analysis of oral diadochokinesis across various ne...

Machine learning-based integration of respiratory oscillometry and spirometry for predicting clinical outcomes in spinal muscular atrophy.

UNLABELLED: Assessing respiratory function in spinal muscular atrophy (SMA) is challenging due to the effort-dependent nature of traditional spirometr...

Brain stroke detection and classification using deep learning-based transCBAMSegNet and hybrid transformer models.

Stroke is a life-threatening neurological condition that requires rapid assessment to reduce mortality and long-term disability. Accurate lesion segme...

When psychological signals meet neurological disability: An explainable XGBoost study of dissociative symptoms in people with multiple sclerosis.

Objective: This study examined whether psychoform and somatoform dissociation, assessed with the Dissociative Experiences Scale-II (DES-II) and the So...

A Multicenter Standardized Gait Database from the ORITEL Network for Cerebral Palsy Gait Analysis.

Gait analysis is a cornerstone of clinical decision-making in cerebral palsy (CP), yet multicenter variability limits comparability and translation. T...

Forecasting Dropout in Home-Based Movement Rehabilitation after Stroke with Sensors and Machine Learning.

Adherence to home-based rehabilitation can support recovery after stroke, yet many patients disengage within the first few weeks. While prior studies ...

Computational neuroelectrophysiology and artificial intelligence for drug-resistant epilepsy: Recent advances, current challenges, and future directions.

Drug-resistant epilepsy (DRE) affects approximately 30% of epilepsy patients, with surgical cure rates below 70%. This challenge drives a fundamental ...

AI-powered evaluation of dementia severity based on clinical data and visual scoring systems (MTA, ERICA, GCA) from MRI.

Dementia, particularly Alzheimer's disease (AD), is a growing concern in aging populations, with mild cognitive impairment (MCI) frequently progressin...