Neurology

Hybrid EEG Feature Fusion Framework for Accurate Autism Spectrum Disorder Diagnosis Using Ensemble Learning.

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with increasing global prevalence and no standardized biological test for early detection. Current diagnosis methods rely heavily on behavioral assessments, which are subjective, time-consuming, and prone to variability. This study proposes a hybrid feature fusion framework for non-invasive ASD diagnosis using electroencephal...

[Technology-driven management of early-onset scoliosis:new technologies and concepts].

Early-onset scoliosis (EOS) refers to spinal deformity occurring in children younger than 10 years of age. The main goals of EOS treatment are not only to correct spinal curvature, but also to preserve spinal and thoracic growth potential. In recent years, the rapid development and clinical application of robotic technology and artificial intelligence (AI) have driven a paradigm shift in EOS manag...

[Applications and prospects of artificial intelligence for decision-making in spinal surgery].

The application of artificial intelligence in decision support for spinal surgery has developed rapidly and now widely covers key steps such as automa...

A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.

Parkinson's disease (PD) is a catastrophic neurodegenerative disorder and a major culprit of neurological disability worldwide. Accurate diagnosis of ...

In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI.

Parkinson's disease (PD) involves pathological iron accumulation, yet MRI metrics, such as R2* or magnetic susceptibility (χ), lack mechanistic specif...

Optimization of attention mechanisms in choral conducting education using EEG and eye movement tracking with transfer learning algorithms.

This study presents a comprehensive investigation into attention mechanism optimization in choral conducting education through the integration of elec...

A clinical neuroimaging platform for rapid, automated lesion detection and personalized post-stroke outcome prediction.

Accurately predicting long-term outcomes after stroke remains a key challenge in personalized medicine. Here, we present a neuroimaging platform that ...

Clinical and EEG features integrated by a deep learning method to investigate treatment outcomes in epilepsy with eyelid myoclonia.

OBJECTIVE: To explore the correlation between clinical and electroencephalogram (EEG) factors and therapeutic outcome in epilepsy with eyelid myocloni...

Image-Quality-Aware Multimodal Artificial Intelligence for Automated Structured OCT Report Generation in Glaucoma Evaluation.

OBJECTIVE: To develop an explainable multimodal large language model (MM-LLM) that (1) screens optic nerve head (ONH) OCT circle scans for quality and...

Multi-dimensional risk prediction and genetic architecture of aspiration pneumonia: a population-based analysis.

BACKGROUND: Aspiration pneumonia represents a significant but understudied cause of morbidity and mortality, particularly among aging populations with...

Feasibility of nonspinal bony landmark-based image registration in CyberKnife ® robotic radiotherapy for pelvic and femoral bone tumors.

In stereotactic body radiotherapy for bone metastases using the CyberKnife system, spinal anatomy is generally employed as the alignment reference. Ho...

Non-invasive somatotopic proprioceptive feedback for closed-loop robotic hand aperture control.

Objective.Current robotic prostheses developed for individuals with transradial amputation often lack physiological feedback, particularly propriocept...

Gray matter brain age predicts cognitive outcome one year after ischemic stroke.

BackgroundPredicting post-stroke cognitive impairment (PSCI) remains challenging.ObjectiveThis study validated two brain age metrics-Gray Matter Brain...

'Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.'.

BACKGROUND: Headache disorders are frequently misdiagnosed. We aimed to systematically evaluate the diagnostic accuracy, methodological quality, and c...

Immersive spatial computing in neurosurgery.

PURPOSE: Immersive spatial computing technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), are increasingly ap...

A Voyage on Computer Aided Intelligent Algorithms for the Segmentation of Brain Tissues for Neurodisorder Diagnosis.

Neurodisorders pose a considerable burden to global health, frequently requiring early treatment and diagnosis to avoid irreversible cognitive and mot...

Machine Learning Model Predicts Clinical Adverse Events of Small Molecule Kinase Inhibitors in Cancer Patients Using On-/Off-Target Engagement and Tissue Selectivity.

Adverse events (AEs) of small molecule kinase inhibitors (SMKIs) at therapeutic doses in cancer patients are largely unpredictable in phase I-III stud...

Artificial Intelligence Tools for Diagnosing the Relationship Between the Inferior Alveolar Nerve and the Third Molar: A Systematic Review.

OBJECTIVE: To evaluate the diagnostic capability of artificial intelligence (AI) compared to human radiographic interpretation in assessing the relati...

Artificial Intelligence in Neuroradiology: A Review of FDA-regulated Algorithms.

Artificial intelligence (AI) is increasingly integrated into neuroradiology practice, with a growing number of FDA-cleared algorithms now supporting t...

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Glaucoma shares similarities with neurodegenerative conditions like dementia, Parkinson's disease, and ischaemic optic neuropathy, which affect ocular...