Neurology

Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.

PURPOSE: To provide a historical review tracing the evolution of oculomics, the study of ocular biomarkers of systemic disease, from early clinical observations to the emergence of advanced imaging and artificial intelligence (AI) technologies over the last century. DESIGN: Narrative historical review. SUBJECTS: Not applicable. METHODS: A review of key historical and technological developments in ...

A multi-view DTI feature fusion framework for enhanced diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is an irreversible neurodegenerative disorder. Diffusion tensor imaging (DTI) is widely used to detect brain alterations for diagnosis, but most methods rely on single-scale information. Therefore, this study proposes the multi-view feature learning framework incorporating residual block-based 3D convolutional neural network (3D-CNN) for AD diagnosis. First, tract-based sp...

Decoding lower-limb movement attempts from electro-encephalographic signals in spinal cord injury patients.

Restoring lower-limb function in patients with severe spinal cord injury (SCI) remains challenging. Spinal cord stimulation may enhance and reinstate ...

Exploring potential biomarkers of NETosis-Related genes in spinal cord injury through machine learning and multi-omics analysis.

Spinal cord injury (SCI) is a serious condition typically caused by mechanical trauma, often resulting in significant motor, sensory, and autonomic dy...

Neural-linguistic analysis for Alzheimer's detection: A deep learning approach informed by cognitive neuroscience.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that disrupts cognitive function across multiple domains, particularly affecting ...

[Advanced imaging diagnosis and therapeutic strategies for ischemic stroke].

In recent years, advances in imaging analysis technologies, including CT perfusion, MRI, and AI analysis, have extended the therapeutic time window fo...

Automated 3D segmentation of human vagus nerve fascicles and epineurium from micro-computed tomography images using anatomy-aware neural networks.

Objective.Precise segmentation and quantification of nerve morphology from imaging data are critical for designing effective and selective peripheral ...

CTSSP: A temporal-spectral-spatial joint optimization algorithm for motor imagery EEG decoding.

Objective.Motor imagery brain-computer interfaces hold significant promise for neurorehabilitation, yet their performance is often compromised by elec...

Explainable End-to-End Seizure Prediction via Dynamic Multiscale Cross-Band Fusion Filter Network.

Epileptic seizure prediction based on electroencephalogram (EEG) signals is one of the critical applications of medical artificial intelligence (AI), ...

Novel Aggressive Subtype of Multiple System Atrophy Identified by Unsupervised Machine Learning.

OBJECTIVES: Multiple system atrophy (MSA) is classified into parkinsonian (MSA-P) and cerebellar (MSA-C) phenotypes based on predominant motor feature...

A preregistered, open pipeline for early cerebral palsy risk assessment from Infant Videos.

Cerebral Palsy (CP), affecting approximately 1 in 500 children due to abnormal brain development, impacts movement control. Early risk assessment via ...

Noise in the diagnosis of epilepsy by experts.

OBJECTIVE: To measure the relative levels of signal and noise in expert diagnosis of epilepsy. METHODS: Twenty multinational epileptologists independe...

Lateralized effects of vagus nerve stimulation on cortical spreading depression: insights from a mouse model.

OBJECTIVES: Vagus nerve stimulation (VNS) is increasingly recognized as a therapeutic approach for neurological disorders, such as epilepsy, migraine,...

Clinical prediction of aortic arch anatomy to guide stroke CT angiography protocols: A machine learning study.

BackgroundThe aortic arch (AA) influences catheter navigation during endovascular treatment (EVT) for acute ischemic stroke. Whether routine inclusion...

Program Theory and Core Outcome Set Development for a Technology-Assisted Counseling Intervention in Dementia: Multimethods Study.

BACKGROUND: Counseling in family dementia care aims to support caregivers in mastering challenges. The use of information and communication technologi...

Quantifying Innovation in Stroke: Large Language Model Bibliometric Analysis.

BACKGROUND: Thrombolysis and mechanical thrombectomy represent the most successful stroke innovations over the last 30 years. Quantifying innovation i...

Preoperative Neuroimaging Markers, Clinical Severity Measures, and Shunt Characteristics for Predicting Shunt Revision in Idiopathic Intracranial Hypertension: An Explainable Machine-Learning Study.

BACKGROUND AND PURPOSE: Surgical shunt placement is a common treatment for idiopathic intracranial hypertension (IIH) but is hampered by high revision...

Multi-scale EEG feature decoding with Swin Transformers for subject independent motor imagery BCIs.

High inter-subject variability and the non-stationary nature of EEG signals pose significant challenges for subject-independent Brain-Computer Interfa...

AI-Assisted detection of corneal nerve structural abnormalities in early diabetic keratopathy: development and validation of a deep learning framework.

To develop a deep learning-based computer-aided diagnostic model for the automated identification of corneal microneuromas from in vivo confocal micro...

Predicting HFA 30-2 Visual Fields with Deep Learning from Multimodal OCT-Fundus Feature Fusion and Structure-Function Discordance Analysis.

Glaucoma is a leading cause of irreversible vision loss. During clinical follow-up, visual field (VF) tests (Humphrey Field Analyzer 30-2) assesses fu...