AI Medical Compendium Journal:
Neurology

Showing 1 to 10 of 40 articles

Automated Whole-Brain Focal Cortical Dysplasia Detection Using MR Fingerprinting With Deep Learning.

Neurology
BACKGROUND AND OBJECTIVES: Focal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging techniq...

Evolution of Cortical Lesions and Function-Specific Cognitive Decline in People With Multiple Sclerosis.

Neurology
BACKGROUND AND OBJECTIVES: Cortical lesions in multiple sclerosis (MS) severely affect cognition, but their longitudinal evolution and impact on specific cognitive functions remain understudied. This study investigates the evolution of function-speci...

A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.

Neurology
BACKGROUND AND OBJECTIVES: Among the participants of Alzheimer disease (AD) treatment trials, 40% do not show cognitive decline over 80 weeks of follow-up. Identifying and excluding these individuals can increase power to detect treatment effects. We...

Prediction of Survival After Pediatric Cardiac Arrest Using Quantitative EEG and Machine Learning Techniques.

Neurology
BACKGROUND AND OBJECTIVES: Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. EEG is frequently used for neuroprognostication in adults, but has not been sufficiently validated f...

Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.

Neurology
BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific eff...

Comparison of Manual vs Artificial Intelligence-Based Muscle MRI Segmentation for Evaluating Disease Progression in Patients With CMT1A.

Neurology
BACKGROUND AND OBJECTIVES: Intramuscular fat fraction (FF), assessed with quantitative MRI (qMRI), has emerged as one of the few responsive outcome measures in CMT1A patients. The main limitation for its use in future therapeutic trials is the time r...

Common Critiques and Recommendations for Studies in Neurology Using Machine Learning Methods.

Neurology
Machine learning (ML) methods are becoming more prevalent in the neurology literature as alternatives to traditional statistical methods to address challenges in the analysis of modern data sets. Despite the increase in the popularity of ML methods i...

Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults.

Neurology
BACKGROUND AND OBJECTIVES: Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential ...

Upper Limb Robots for Recovery of Motor Arm Function in Patients With Stroke: A Systematic Review and Meta-Analysis.

Neurology
BACKGROUND AND OBJECTIVES: Robot technology to support upper limb (UL) rehabilitation poststroke has rapidly developed over the past 3 decades. We aimed to assess the effects of UL-robots (UL-RTs) on recovery of UL motor functioning and capacity post...