Neurology

Stereotactic Body Radiotherapy for Spinal Metastases: Outcomes and Development of a Risk Assessment Score to Predict Local Tumor Progression.

BACKGROUND AND OBJECTIVES: Stereotactic body radiotherapy (SBRT) has emerged as an effective treatme...

Exploration of a Multimodal Machine Learning Model Integrating Ultrasound and Clinical Indicators for the Diagnosis of Diabetic Peripheral Neuropathy.

OBJECTIVES: Based on ultrasound technology and clinical indicators, this study intends to develop mu...

Population-Level Digital Stroke Surveillance: Building a Fair and Accurate ICD-10 Detection Model.

Background The International Classification of Diseases, 10th Revision (ICD-10), is widely used for ...

Mitochondrial dysfunction and programmed cell death in Alzheimer's disease: A retrospective bioinformatics study.

Alzheimer's disease (AD) is a major cause of dementia, and this paper explores the unclear roles of ...

Developing and validating a clinically actionable prediction tool for Parkinson disease using explainable machine learning with multidimensional national data: A cross-sectional study.

Parkinson disease (PD) is a rapidly growing neurodegenerative disorder that presents a significant p...

Scalable and Robust Artificial Intelligence for Spine Alignment Assessment: Multicenter Study Enabled by Real-Time Data Transformation.

BACKGROUND: Artificial intelligence (AI) has shown promise for automating spinal alignment assessmen...

Effect of a clinical decision support system on stroke care quality and outcomes in patients with acute ischaemic stroke (GOLDEN BRIDGE II): cluster randomised clinical trial.

OBJECTIVE: To evaluate the efficacy of a clinical decision support system (CDSS) on stroke care qual...

Dense and distributed neuropeptide network in the nerve net of Hydra vulgaris.

Neuroscience has long emphasized synaptic transmission and physical wiring as the substrate of brain...

Cognitive function is associated with risk of conversion to secondary progressive multiple sclerosis: A cross-sectional study.

BACKGROUND: Predicting conversion from relapsing-remitting multiple sclerosis (RRMS) to secondary pr...

Research on epilepsy detection and recognition based on the combination of time frequency transform and deep learning model.

To improve the detection performance of epileptic electroencephalogram (EEG) signals and address the...

Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.

Schizophrenia (ScZ) is a growing global health concern that affects millions of people and puts seve...

Profiling of hypoxic and extracellular matrix remodeling markers in biofluids and cerebral tissue after traumatic brain injury: expression and signaling crosstalk of HIF-1α, MMP-9/14 and EPHA2.

Traumatic brain injury (TBI) remains a formidable clinical, neuropathological, and forensic challeng...

Corrigendum to: A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis.

In the originally published article entitled "A Comprehensive Review on Deep Learning Techniques in ...

Machine learning-based analysis of prognostic factors in patients with tuberculous meningitis.

Predicting self-image satisfaction after adult spinal deformity surgery: a machine learning approach using patient phenotypes.

PURPOSE: To characterize the evolution of the Self-Image (SI) domain of the Scoliosis Research Socie...

Visualization of the Trochlear Nerve Using Deep Learning-enhanced 3D T2-weighted MR Imaging at 3T.

PURPOSE: Cranial nerve imaging with 3T MRI commonly uses 3D fast imaging employing steady-state acqu...

Independent evaluation of deep learning models for detecting focal cortical dysplasia.

The purpose of this study is to perform an independent assessment of three state-of-the-art tools fo...

An AI-based nomogram (the CGRP score) for the prediction of response to modern migraine therapies: an independent assessment and some considerations on the application of AI tools in clinical setting.

Past, present and future perspectives of traumatic brain injury.

Despite extensive research, traumatic brain injury (TBI) imposes significant mortality and morbidity...

MAGCANet: A multiscale adaptive graph-convolutional attention network for MI-EEG decoding.

OBJECTIVE: Motor imagery EEG (MI-EEG) decoding remains challenging due to low signal-to-noise ratios...

Development and Validation of a Machine Learning Model to Predict Oral Anticoagulant Use in Stroke From Prothrombin Time-International Normalized Ratio and Activated Partial Thromboplastin Time.

BACKGROUND: Timely recognition of oral anticoagulant use is critical in acute stroke but is often ha...