Latest AI and machine learning research in neurology for healthcare professionals.
Early and accurate detection of Parkinson's disease (PD) remains a critical challenge in medical diagnostics due to the subtlety of early-stage symptoms and the complex, non-linear relationships inherent in biomedical data. Traditional machine learning (ML) models, though widely applied to PD detection, often rely on extensive feature engineering and struggle to capture complex feature interaction...
Normative modeling learns a healthy reference distribution and quantifies subject-specific deviations to capture heterogeneous disease effects. In Alzheimers disease (AD), multimodal neuroimaging offers complementary signals but VAE-based normative models often (i) fit the healthy reference distribution imperfectly, inflating false positives, and (ii) use posterior aggregation (e.g., PoE/MoE) that...
Background: Identifying early brain-based markers of cognitive decline is critical for preventive strategies in Alzheimer's disease. Individuals with ...
Background and Purpose Embolic stroke of undetermined source (ESUS) emains a major diagnostic challenge in vascular neurology, as a substantial propor...
Aging and genetic risk shape the molecular programs that confer cellular vulnerability in Alzheimer's disease (AD), but whether these programs differ ...
Atypical Parkinsonian Disorders (APD), also known as Parkinson-plus syndrome, are a group of neurodegenerative diseases that include progressive supra...
This work examines a disc-centric approach for automated severity grading of lumbar spinal stenosis from sagittal T2-weighted MRI. The method combines...
Semantic representations can be framed as a structured, dynamic knowledge space through which humans navigate to retrieve and manipulate meaning. To i...
Parkinsons disease is a progressive neurological disorder characterized by motor impairments whose severity is commonly assessed using the Unified Par...
The spinal cord contains evolutionarily conserved cell types critical for motor function, sensory processing, and autonomic regulation, many of which ...
Mitochondrial dysfunction is implicated in a wide range of disorders, including cancer, neurodegeneration, and cardiovascular diseases. Conventional a...
Aphasia, an acquired language deficit, is the most common post-stroke focal cognitive impairment, and roughly 60% cases become chronic (duration >6 mo...
BACKGROUND AND OBJECTIVES: Effective education on Alzheimer's disease (AD) requires methods fostering empathy, confidence, and knowledge. Artificial i...
Timely and accurate diagnosis of dementia remains a critical yet challenging task. Although machine learning (ML) techniques have shown considerable p...
Background: Amyotrophic lateral sclerosis (ALS) is clinically heterogeneous, and genetic modifiers may drive molecular endophenotypes without obvious ...
Early detection of dementia enables timely intervention and better care planning. Electroencephalography, being accessible and noninvasive, offers a p...
Accurate and timely seizure detection from Electroencephalography (EEG) is critical for clinical intervention, yet manual review of long-term recordin...
Large language models (LLMs) have achieved remarkable capabilities, yet methods to verify which model components are truly necessary for language func...
Genetic-based risk prediction is becoming increasingly available for a wide range of common diseases thanks to the growth of large-scale biobanks and ...
Magnetic resonance imaging (MRI) is a cornerstone of modern neuroimaging, where accurate segmentation of brain structures and lesions is essential for...