Neurology

Therapeutic Mechanisms of Medicine Food Homology Plants in Alzheimer's Disease: Insights from Network Pharmacology, Machine Learning, and Molecular Docking.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by a gradual decline in cognitive function. Currently, there are no effective treatments for this condition. Medicine food homology plants have gained increasing attention as potential natural treatments for AD because of their nutritional value and therapeutic benefits. In this work, we aimed to provide a deeper un...

Prediction and validation of anoikis-related genes in neuropathic pain using machine learning.

BACKGROUND: Neuropathic pain (NP) can be induced by a variety of clinical conditions, such as spinal cord injury, lumbar disc herniation (LDH), lumbar spinal stenosis, diabetes, herpes zoster, and spinal cord tumors, and inflammatory stimuli. The pathogenesis of NP is extremely complex. Specifically, in LDH, the herniated nucleus pulposus exerts mechanical pressure on nerve roots, triggering local...

Evaluation of AI-based nerve segmentation on ultrasound: relevance of standard metrics in the clinical setting.

BACKGROUND: In artificial intelligence for ultrasound-guided regional anaesthesia, accurate nerve identification is essential. The technology communit...

CycleH-CUT: an unsupervised medical image translation method based on cycle consistency and hybrid contrastive learning.

Unsupervised medical image translation tasks are challenging due to the difficulty of obtaining perfectly paired medical images. CycleGAN-based method...

Artificial Intelligence and Predictive Modeling in the Management and Treatment of Episodic Migraine.

PURPOSE OF REVIEW: Artificial intelligence (AI) has impacted different aspects of headache medicine, from history taking and diagnosis to drug develop...

Estimation of Machine Learning-Based Models to Predict Dementia Risk in Patients With Atherosclerotic Cardiovascular Diseases: UK Biobank Study.

BACKGROUND: The atherosclerotic cardiovascular disease (ASCVD) is associated with dementia. However, the risk factors of dementia in patients with ASC...

Development and validation of a machine learning approach for screening new leprosy cases based on the leprosy suspicion questionnaire.

Leprosy is a dermatoneurological disease and can cause irreversible nerve damage. In addition to being able to mimic different rheumatological, neurol...

CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly ...

Interpretable modality-specific and interactive graph convolutional network on brain functional and structural connectomes.

Both brain functional connectivity (FC) and structural connectivity (SC) provide distinct neural mechanisms for cognition and neurological disease. In...

Linguistic cues for automatic assessment of Alzheimer's disease across languages.

BackgroundMost common forms of dementia, including Alzheimer's disease, are associated with alterations in spoken language.ObjectiveThis study explore...

Of Pilots and Copilots: The Evolving Role of Artificial Intelligence in Clinical Neurophysiology.

Artificial intelligence (AI) is revolutionizing clinical neurophysiology (CNP), particularly in its applications to electroencephalography (EEG), elec...

Comprehensive clinical scale-based machine learning model for predicting subthalamic nucleus deep brain stimulation outcomes in Parkinson's disease.

Parkinson's Disease (PD) is a growing burden with varied clinical manifestations and responses to Subthalamic Nucleus Deep Brain Stimulation (STN-DBS)...

Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study.

BACKGROUND: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Kno...

A feature explainability-based deep learning technique for diabetic foot ulcer identification.

Diabetic foot ulcers (DFUs) are a common and serious complication of diabetes, presenting as open sores or wounds on the sole. They result from impair...

Urban and rural disparities in stroke prediction using machine learning among Chinese older adults.

Stroke is a significant health concern in China. Differences in stroke risk between rural and urban areas have been highlighted in prior research. How...

Hybrid CNN-GRU Models for Improved EEG Motor Imagery Classification.

Brain-computer interfaces (BCIs) based on electroencephalography (EEG) enable neural activity interpretation for device control, with motor imagery (M...

Deep brain stimulation of the nucleus basalis of Meynert in neurodegenerative diseases with cognitive impairment: An update on evidence and mechanisms.

Current pharmacotherapy for neurodegenerative diseases is limited to providing symptomatic relief, instead of slowing or reversing disease progression...

Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.

Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individual...

Inductive reasoning with large language models: A simulated randomized controlled trial for epilepsy.

INTRODUCTION: To investigate the potential of using artificial intelligence (AI), specifically large language models (LLMs), for synthesizing informat...

Electroencephalogram (EEG) Based Fuzzy Logic and Spiking Neural Networks (FLSNN) for Advanced Multiple Neurological Disorder Diagnosis.

Neurological disorders are a major global health concern that have a substantial impact on death rates and quality of life. accurately identifying a n...