Latest AI and machine learning research in neurology for healthcare professionals.
This study addresses the challenge of selective auditory attention in noisy environments by proposing an electroencephalography (EEG)-based target speaker extraction model, ASEAF, designed to mimic neural decoding through tailored spatio-temporal feature extraction and cross-modal fusion. The model achieves precise extraction of the target speaker's speech by simultaneously processing EEG and audi...
BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions of people worldwide. It severely impairs patients' mobility. For effective treatment strategies, it is essential to determine the severity of the disease at an early stage. METHODS: In this study, an optimized feature-fusion framework is developed to detect the severity of Parkinson's disease using ...
The updated German Society of Cardiology (DGK) position paper on catheter ablation of atrial fibrillation (AF) [1] presents the current evidence, tech...
Preparing nursing students to provide high-quality, person-centered dementia care is a global priority, yet most research has focused on measuring edu...
Brain-Computer Interface (BCI) technology, integrating neuroscience and artificial intelligence, has been widely applied in neural rehabilitation. How...
A decrease in Minimum Foot Clearance (MFC), which represents the minimum vertical distance of the foot from the ground surface during the swing phase ...
BACKGROUND: Dyslipidemia is a multifactorial and complex condition that warrants investigation through advanced analytical approaches such as machine ...
BACKGROUND: Timely medical follow-up after a diagnosis of cognitive impairment, such as mild cognitive impairment (MCI) or dementia, is imperative for...
This scoping review synthesizes evidence on fluid and neuroimaging biomarkers for preclinical and early Alzheimer's disease (AD)-including mild cognit...
BACKGROUND: Myofascial trigger points (MTrPs), which arise from muscle overload and subsequent ischemia, contribute to myofascial pain syndrome (MPS)....
BACKGROUND AND PURPOSE: Ischemic stroke comprises about 87% of all stroke cases in the US. 20% of these have a cardioembolic (CE) etiology, and 25% ar...
The assessment of depression severity still relies primarily on subjective rating scales, with a lack of objective quantitative biomarkers. This study...
Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and heal...
Major depressive disorder (MDD) is highly prevalent among adolescents, but its neurobiological mechanisms remain unclear. Neuroimaging studies have sh...
Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders. Recent statistical surveys and studies indicate that AD is poised t...
The lack of validated stage-specific biomarkers hampers the understanding of Alzheimer's disease (AD) progression and clinical translation. Current tr...
This paper proposes a Prototype-Guided Deformable Memory Transformer (Proto-MemFormer) model for Parkinson's Disease (PD) MRI classification. In the e...
Needle electromyography (nEMG) is a valuable tool for diagnosing patients with neuromuscular diseases. However, it is labor-intensive and is prone to ...
Iron (Fe) and copper (Cu) are vital micronutrients that regulate many critical physiological processes in the human body, with their homeostasis in th...
BACKGROUND: Chronic systemic inflammation is a pivotal modifiable risk factor for stroke. The food-based Food Inflammation Index (FII) offers a novel ...