Latest AI and machine learning research in neurology for healthcare professionals.
BACKGROUND: Deep-learning models are capable of predicting age from retinal scans and the difference between this and chronological age, retinal age gap, has been shown to be significantly associated with risk of mortality, cardiovascular diseases, and kidney failure. OBJECTIVE: Our aim was to investigate the association between Parkinson's disease (PD) and retinal age gap in a large, real-world p...
Postoperative delirium is associated with both gut microbiota alterations and Tau phosphorylation; however, how these factors interact and jointly contribute to postoperative delirium remains poorly understood. This prospective observational cohort study screened 491 patients aged ≥65 years undergoing elective laminectomy or hip or knee replacement under general or spinal anesthesia at Massachuset...
BACKGROUND AND AIMS: Accurate stroke-risk stratification is central to anticoagulation decision-making in patients with atrial fibrillation (AF), but ...
The study aimed to develop and optimize chitosan-based mucoadhesive nanomicelles for intranasal delivery of lamotrigine (LTG), to enhance epilepsy tre...
BACKGROUND: Baseline neurofilament light chain (NFL) predicts amyotrophic lateral sclerosis (ALS) outcomes, but it does not summarize early repeated b...
BACKGROUND: Previous research links frailty to cognitive decline, but the relationship between frailty and motoric cognitive risk syndrome (MCR), a de...
Manual segmentation of orbital magnetic resonance imaging (MRI) is labor-intensive, hindering large-scale morphometric studies. To overcome this, we d...
Synaptic dysfunction is a major driver of cognitive decline in Alzheimer's disease (AD), yet its extent and molecular basis in the retina remain poorl...
Characterizing surface protein heterogeneity on extracellular vesicles remains challenging but essential for understanding their biological functions ...
Background: EEG responses to violence-related visual stimuli are relevant to neuroscience and digital forensics. Yet most EEG classification models em...
Encephalitic alphaviruses such as Western equine encephalitis virus (WEEV) result in significant morbidity through acute viremia and postencephalitic ...
Multimodal physiological signal fusion-particularly electroencephalography (EEG) and electrocardiography (ECG)-is widely assumed to improve emotion re...
Accumulation of abnormal tau protein into neurofibrillary tangles (NFTs) is a pathologic hallmark of Alzheimer disease (AD). Accurate detection of NFT...
BACKGROUND AND PURPOSE: Medical chart abstraction plays a critical role in clinical research and quality monitoring by transforming unstructured narra...
OBJECTIVES: To evaluate whether the deep learning model IGENet-TS, a time-domain convolutional neural network (CNN), can classify expert-selected EEG ...
OBJECTIVE: To develop and evaluate machine learning-based models for predicting fall risk within 6 months of stroke onset. METHODS: This prospective s...
Traumatic brain injury (TBI) is a major cause of mortality and persistent neurological disability and is increasingly recognized as a potential contri...
As dementia prevalence rises globally, health sciences education must evolve to prepare future professionals across disciplines to provide person-cent...
Seizure prediction is critically important, as it can help prevent serious injuries, improve quality of life, and potentially reduce the risk of SUDEP...
Precision psychiatry seeks to improve individual-level prediction, treatment selection, monitoring, and prevention by integrating clinical, biological...