AIMC Topic: Nervous System Diseases

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Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases.

Scientific reports
Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to constru...

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.

Scientific reports
Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases cause minor changes in the brain's anatomy. Magnetic Resonance Imaging (MRI) is a vital tool for diag...

γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.

Military Medical Research
γ neuromodulation has emerged as a promising strategy for addressing neurological and psychiatric disorders, particularly in regulating executive and cognitive functions. This review explores the latest neuromodulation techniques, focusing on the cri...

Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.

NAR genomics and bioinformatics
Large-scale quantitative studies have identified significant genetic associations for various neurological disorders. Expression quantitative trait locus (eQTL) studies have shown the effect of single-nucleotide polymorphisms (SNPs) on the differenti...

Automated inference of disease mechanisms in patient-hiPSC-derived neuronal networks.

Communications biology
Human induced pluripotent stem cells (hiPSCs)-derived neuronal networks on multi-electrode arrays (MEAs) are a powerful tool for studying neurological disorders. The electric activity patterns of these networks differ between healthy and patient-deri...

Validation of an Artificial Intelligence-Powered Virtual Assistant for Emergency Triage in Neurology.

The neurologist
OBJECTIVES: Neurological emergencies pose significant challenges in medical care in resource-limited countries. Artificial intelligence (AI), particularly health chatbots, offers a promising solution. Rigorous validation is required to ensure safety ...

Significance of NMDA receptor-targeting compounds in neuropsychological disorders: An in-depth review.

European journal of pharmacology
N-methyl-D-aspartate receptors (NMDARs), a subclass of glutamate-gated ion channels, play an integral role in the maintenance of synaptic plasticity and excitation-inhibition balance within the central nervous system (CNS). Any irregularities in NMDA...

Documentation, Coding, and Billing for Neurologic Services and Procedures.

Seminars in neurology
Documentation, coding, and billing (claims submission) are foundational to neurologic practice in the United States, enabling accurate reimbursement, effective communication, and data-driven advancements in patient care, research, and education. Neur...

Computational modelling for risk assessment of neurological disorder in diabetes using Hodgkin-Huxley model.

Computer methods and programs in biomedicine
BACKGROUND: Diabetes mellitus, characterized by chronic glucose dysregulation, significantly increases the risk of neurological disorders such as cognitive decline, seizures, and Alzheimer's disease. As neurons depend on glucose for energy, fluctuati...

An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Scientific reports
Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observations, which can make it difficult to examine mental health illnesses due to their subjective and complicated nature. In this work, we offer an innovative...