Neurology

Development and multicenter validation of a machine learning model for postoperative sepsis risk in critically Ill traumatic spinal injury patients.

OBJECTIVE: To develop and validate a machine learning model for postoperative sepsis in critically ill traumatic spinal injury (TSI) patients, a frequent and severe complication without dedicated predictive tools. METHODS: Model development used the MIMIC-IV 3.1 database, with external validation in the eICU-CRD 2.0 database and a Chinese TSI cohort. Variables documented within 24 h of postoperati...

Systematic machine learning approach for cerebral aneurysm feature selection and rupture status classification.

BACKGROUND: Current methods of intracranial aneurysm rupture risk assessment in the clinical setting depend on user measurements of morphological factors. Recently, machine learning (ML) has been proposed to assist in stratification of aneurysmal rupture risk. While numerous approaches have been taken, most rely on small sample sizes, restrict to specific anatomical locations, or manually balance ...

Quantitative EEG enhances early assessment and prognostic stratification of brain dysfunction in infants with abusive head trauma.

AIM: To assess the value of quantitative EEG (qEEG) as a diagnostic and prognostic biomarker in infants with abusive head trauma (AHT). Despite its ce...

Transcriptomic analysis of plasma small extracellular vesicles identifies potential diagnostic biomarkers for Parkinson's disease dementia.

INTRODUCTION: Blood-based biomarkers that can aid diagnosis of Parkinson's Disease (PD) dementia (PDD), and predict PDD onset in people with PD are ur...

Machine learning models for CT-based classification of ischemic stroke onset time within or beyond 4.5 h: a comparison of approaches.

OBJECTIVES: To compare machine learning models using different combinations of clinical and imaging variables for classifying ischemic stroke patients...

Jointly Learned 3D Non-Cartesian Sampling With Wave Encoding and Reconstruction for Neurovascular Phase Contrast MRI.

PURPOSE: To develop accelerated 3D phase contrast (PC) MRI using jointly learned wave encoding and reconstruction. METHODS: Pseudo-fully sampled neuro...

Intelligent electroencephalogram feature engineering for rapid mental health diagnosis.

Schizophrenia is one of the serious disorders and, if left untreated, can result in a range of problems with cognition, behavior, and emotions that af...

Using, misusing, and improving online machine learning-based meta-analysis of neuroimaging published data: A perspective on NeuroQuery.

Online, text-based meta-analysis tools for large databases represent a new digital advance for medical, health, and neuroscience research, among other...

AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques.

Alzheimer's disease (AD) is a degenerative neurological disease that progresses over time, making early detection crucial for effective intervention a...

Technical Review of Magnetic Resonance Fingerprinting Applications in Cerebral Physiology.

Magnetic resonance fingerprinting (MRF) enables quantitative MRI by allowing the simultaneous mapping of multiple tissue properties through innovative...

Persistent dysmorphology and dysfunction of the brain microvasculature following repeated trauma.

Mild traumatic brain injury typically produces no abnormalities on neuroimaging yet elicits symptoms that, in an increasing fraction of survivors, lin...

Prediction of Postherpetic Neuralgia in Patients with Acute and Subacute Herpetic Neuralgia Using Structural Magnetic Resonance Imaging: A Retrospective Study.

INTRODUCTION: This study integrated structural magnetic resonance imaging (sMRI) of the brain with clinical characteristics to identify the "vulnerabl...

Enlarged Perivascular Spaces and Modifiable Vascular Risk Factors: Cross-Sectional and Longitudinal Analysis in the UK Biobank Cohort.

OBJECTIVE: To examine cross-sectional and longitudinal associations between vascular risk factors, APOE genotype, and perivascular spaces (PVS), with ...

An event-based filtering and weighted enhanced deep learning epileptic seizure prediction method.

The timely detection of impending seizures can offer physicians a critical window of opportunity to implement interventions and enable epileptic patie...

Using computational semantics to study meaning in the brain.

How do humans understand the meaning of individual words? How do we combine the meaning of multiple words to comprehend novel sentences? Cognitive neu...

Profiling the Braak progression in Parkinson's disease: a transcriptomics and ML driven identification of progressive biomarker and prognostic ceRNA signature.

Parkinson's disease (PD), the second most prevalent neurodegenerative disorder, is marked by dopaminergic neuron loss and α-synuclein aggregation. Alt...

Integrating administrative health data and machine learning to predict ALS onset.

BACKGROUND: This study aims to develop a Machine Learning (ML) model to predict the initial diagnosis of Amyotrophic Lateral Sclerosis (ALS). METHODS:...

Risk Models for Adverse Events in Microsurgery for Intracranial Unruptured Aneurysms.

BACKGROUND AND OBJECTIVES: Preventive treatment of unruptured intracranial aneurysms (UIAs) requires assessment of treatment risks vs expected benefit...

Enhancing Asleep Deep Brain Stimulation Targeting for Essential Tremor Using Machine Learning: The OPTIVIM Phase 2 Study.

BACKGROUND AND OBJECTIVES: Deep brain stimulation (DBS) is an effective treatment of essential tremor, but the optimal target and how to reach it with...

Balancing Innovation and Responsibility: Ethical and Privacy Challenges in Stroke Digital Health.

Digital health solutions are being increasingly used to support care delivery across various medical fields. In stroke care, technology has contribute...