Neurology

Machine learning identified novel players in lipid metabolism, endosomal trafficking, and iron metabolism of the ALS spinal cord.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease affecting motor neurons. Although genes causing familial cases have been identified, those of sporadic ALS, which occupies the majority of patients, are still elusive. In this study, we adopted machine learning to build binary classifiers based on the New York Genome Center (NYGC) ALS Consortium's RNA-seq data of the postmort...

Predicting functional outcomes of patients with spontaneous intracerebral hemorrhage based on explainable machine learning models: a multicenter retrospective study.

BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is the second most common cause of cerebrovascular disease after ischemic stroke, with high mortality and disability rates, imposing a significant economic burden on families and society. This retrospective study aimed to develop and evaluate an interpretable machine learning model to predict functional outcomes 3 months after SICH.

A novel muscle network approach for objective assessment and profiling of bulbar involvement in ALS.

INTRODUCTION: As a hallmark feature of amyotrophic lateral sclerosis (ALS), bulbar involvement significantly impacts psychosocial, emotional, and phys...

A novel machine learning based framework for developing composite digital biomarkers of disease progression.

BACKGROUND: Current methods of measuring disease progression of neurodegenerative disorders, including Parkinson's disease (PD), largely rely on compo...

Risk prediction for elderly cognitive impairment by radiomic and morphological quantification analysis based on a cerebral MRA imaging cohort.

OBJECTIVE: To establish morphological and radiomic models for early prediction of cognitive impairment associated with cerebrovascular disease (CI-CVD...

Machine Learning Approach for Sepsis Risk Assessment in Ischemic Stroke Patients.

BackgroundIschemic stroke is a critical neurological condition, with infection representing a significant aspect of its clinical management. Sepsis, a...

Deep Learning-Based Segmentation of Cervical Posterior Longitudinal Ligament Ossification in Computed Tomography Images and Assessment of Spinal Cord Compression: A Two-Center Study.

OBJECTIVE: This study aims to develop a fully automated, computed tomography (CT)-based deep learning (DL) model to segment ossified lesions of the po...

A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data.

Inter-individual variability in symptoms and the dynamic nature of brain pathophysiology present significant challenges in constructing a robust diagn...

Prediction of delirium occurrence using machine learning in acute stroke patients in intensive care unit.

INTRODUCTION: Delirium, frequently experienced by ischemic stroke patients, is one of the most common neuropsychiatric syndromes reported in the Inten...

A Fine-grained Hemispheric Asymmetry Network for accurate and interpretable EEG-based emotion classification.

In this work, we propose a Fine-grained Hemispheric Asymmetry Network (FG-HANet), an end-to-end deep learning model that leverages hemispheric asymmet...

PADS-Net: GAN-based radiomics using multi-task network of denoising and segmentation for ultrasonic diagnosis of Parkinson disease.

Parkinson disease (PD) is a prevalent neurodegenerative disorder, and its accurate diagnosis is crucial for timely intervention. We propose the PArkin...

Unveiling neural activity changes in mild cognitive impairment using microstate analysis and machine learning.

BACKGROUND: Mild cognitive impairment (MCI) is recognized as a condition that may increase the risk of developing Alzheimer's disease (AD). Understand...

Utilizing natural language processing to identify pediatric patients experiencing status epilepticus.

PURPOSE: Compare the identification of patients with established status epilepticus (ESE) and refractory status epilepticus (RSE) in electronic health...

Designing a Multimodal and Culturally Relevant Alzheimer Disease and Related Dementia Generative Artificial Intelligence Tool for Black American Informal Caregivers: Cognitive Walk-Through Usability Study.

BACKGROUND: Many members of Black American communities, faced with the high prevalence of Alzheimer disease and related dementias (ADRD) within their ...

Deep Learning-Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction.

BACKGROUND AND PURPOSE: DWI is crucial for detecting infarction stroke. However, its spatial resolution is often limited, hindering accurate lesion vi...

Multimodal fuzzy logic-based gait evaluation system for assessing children with cerebral palsy.

Gait analysis is crucial for identifying functional deviations from the normal gait cycle and is essential for the individualized treatment of motor d...

Artificial intelligence empowered voice generation for amyotrophic lateral sclerosis patients.

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that can result in a progressive loss of speech due to bulbar dysfunction, which ca...

VoxelMorph-Based Deep Learning Motion Correction for Ultrasound Localization Microscopy of Spinal Cord.

Accurate assessment of spinal cord vasculature is important for the urgent diagnosis of injury and subsequent treatment. Ultrasound localization micro...

EEG microstate analysis and machine learning classification in patients with obsessive-compulsive disorder.

BACKGROUND: Microstate characterization of electroencephalogram (EEG) is a data-driven approach to explore the functional changes and interrelationshi...

A bioinspired fish fin webbing for proprioceptive feedback.

The propulsive fins of ray-finned fish are used for large scale locomotion and fine maneuvering, yet also provide sensory feedback regarding hydrodyna...