Neurology

Identification and validation of key genes as diagnostic biomarkers for epilepsy using bioinformatics and machine learning.

Epilepsy is a common neurological disorder with a complex molecular pathogenesis. Approximately one-third of patients experience drug-resistant seizures, which severely impairs their quality of life. This study employed an integrated bioinformatics and machine learning approach to identify key genes associated with epilepsy. Epilepsy-related datasets from the GEO database were analyzed using diffe...

Graph empirical mode decomposition and multiscale feature extraction for EEG-based classification of Alzheimer's disease and frontotemporal dementia.

BACKGROUND AND OBJECTIVE: Early and correct classification of neurodegenerative diseases like Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is one of the most important challenges in clinical neurology. In this paper, we present a novel electroencephalogram (EEG)-based approach that integrates a rich set of multiresolution features to improve the performance of automatic classificatio...

Artificial Intelligence-Assisted Anatomical Recognition System-Guided Robotic Total Mesorectal Excision and Autonomic Nerve Preservation for Rectal Neuroendocrine Tumor.

Regional brain age is decreased in children with sickle cell anemia.

DeepBrainNet, a machine learning tool, uses magnetic resonance imaging (MRI) to predict an individual's brain age, allowing calculation of the brain a...

Enhanced Identification of Chronic Ankle Instability Under Different Conditions: A New Evaluation Framework Based on Feature Fusion and Machine Learning.

This study proposed a synergy-informed evaluation framework that integrates muscle synergy features derived from non-negative matrix factorization (NN...

Machine Learning Models for Mortality Prediction in Intensive Care Unit Patients With Ischemic Stroke Associated With Intracranial Artery Stenosis: Retrospective Cohort Study.

BACKGROUND: Mortality prediction in intensive care unit (ICU) patients with ischemic stroke complicated by intracranial artery stenosis or occlusion r...

Generative artificial intelligence in dementia care: a scoping review protocol on nursing roles, experiences and ethical perspectives.

INTRODUCTION: Generative artificial intelligence (GAI), including large language models and multimodal generative systems, is rapidly emerging in heal...

Robust learning framework for a scalable remote monitoring of autonomic dysreflexia: use-case in spinal cord injury.

The autonomous nervous system (ANS) response in neurological disorders is a direct modifiable risk factor for cardiovascular health, however, difficul...

Regional brain age is decreased in children with sickle cell anemia.

DeepBrainNet, a machine learning tool, uses magnetic resonance imaging (MRI) to predict an individual's brain age, allowing calculation of the brain a...

The prognostic value of the PALBI score for mortality prediction in stroke: an internal and external validation study.

Recent studies show that there's a link between liver problems and how well someone does after having a stroke. The platelet-albumin-bilirubin (palbi)...

Screening Biomarkers for Nerve Injury Using Weighted Gene Co‐Expression Network Analysis and Machine Learning.

BACKGROUND: Nerve injury triggers complex molecular responses involving immune activation and neuronal damage, yet the key regulatory genes and their ...

TASNet: A tri-modal attentive scale robust adaptive fusion framework for progressive mild cognitive impairment classification.

BACKGROUND: Accurate prediction of progressive Mild Cognitive Impairment (pMCI) versus stable MCI (sMCI) is crucial for early Alzheimer's disease (AD)...

Reliable detection of focal onset impaired awareness seizures in patients with epilepsy using wearable ECG: Development and validation study.

BACKGROUND: Underreporting of seizures, particularly focal onset impaired awareness seizures (FIAS), compromises the effectiveness of patient care and...

A Deep Learning Model for Artery of Adamkiewicz Detection in Bronchial Artery Embolization: A Multicenter Retrospective Study.

PURPOSE: To evaluate the effectiveness of a deep learning model for recognizing the artery of Adamkiewicz and anterior spinal artery (ASA) to prevent ...

Poincaré feature-based classification of electroencephalography signals for multiple sclerosis diagnosis.

BACKGROUND: As one of the most widespread neurodegenerative disorders, Multiple Sclerosis (MS) is a progressive neuroinflammatory disorder affecting m...

Classification of ALS molecular subtypes: a literature review on machine learning applications and their clinical value.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterised by considerable heterogeneity in both its underlyin...

Machine Learning Integration of MRI Intratumoral and Peritumoral Radiomics Features for Predicting PNSTs Postoperative Complications.

RATIONALE AND OBJECTIVES: Postoperative complications (15-76%) substantially affect patients with peripheral nerve sheath tumors (PNSTs), yet objectiv...

From muscle to motion: GaIn nanoparticle-TPU core/shell mesh electrodes for intelligent prosthesis.

Electromyography (EMG) electrodes are critical for detecting and interpreting muscle activity, which is essential for operating prosthetic devices and...

Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.

This narrative review maps the current landscape of artificial intelligence (AI) in paediatric and fetal neuroradiology, critically evaluating current...

Sleep disturbances and Alzheimer's disease: a multiscale approach from exposome to neurobiology and precision medicine.

Sleep disturbances and Alzheimer's disease (AD) are interconnected public health challenges. However, the underlying mechanisms of their complex relat...