Latest AI and machine learning research in neurology for healthcare professionals.
Alzheimer's disease (AD) classification using machine learning has increasingly relied on multimodal inputs such as Magnetic Resonance Imaging (MRI), cognitive assessments, and biological markers. This study evaluates whether integrating these sources enhances predictive performance compared to using them independently. Neural networks were trained on data from the Alzheimer's Disease Neuroimaging...
BACKGROUND: Delayed cerebral ischemia (DCI) remains a major morbidity and mortality problem following aneurysmal subarachnoid hemorrhage (SAH). Advancements in neurocritical care permit a slow but accurate identification of patients at high risk for DCI. Machine learning models are now emerging as tools for DCI prediction that may provide more individualized risk assessment than conventional appro...
Parkinson's disease (PD) is a degenerative neurological condition defined by a wide range of motor and non-motor symptoms that can affect function to ...
BACKGROUND: Parkinson's disease (PD) is a common neurodegenerative disorder characterised by high prevalence and disability rates, severely impairing ...
Neurodegenerative diseases, like Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), a...
INTRODUCTION/AIMS: The cross-sectional area (CSA) of the median nerve (MN) is a key parameter for confirming carpal tunnel syndrome (CTS) with ultraso...
Deep learning models leveraging human activity data, such as gait, have shown promise for dementia prediction. However, their limited interpretability...
The detection of Alzheimer's Disease (AD) using structural Magnetic Resonance Imaging (MRI) and Machine Learning (ML) often focuses on late-stage atro...
Understanding how self-confidence fluctuates during cognitive activity and how these fluctuations relate to objective physiological signals remains a ...
Allostatic load scores (ALSs) quantify the cumulative physiological burden of sustained stress across neuro-endocrine, metabolic, cardiovascular and i...
PURPOSE: To evaluate the feasibility and technical performance of integrating a Delay Alternating with Nutation for Tailored Excitation (DANTE) prepar...
Alzheimer's disease (AD) has a strong genetic predisposition. Genome-wide association studies have identified multiple risk loci, yet many non-coding ...
BACKGROUND: Plasma biomarkers have emerged as robust indicators of Alzheimer's disease (AD) pathology, offering accessible tools for staging and strat...
BACKGROUND: Epigenetic modifications play a vital role in the pathogenesis of human diseases, particularly neurodegenerative disorders such as Alzheim...
INTRODUCTION: Insulin resistance is a complex metabolic disorder that involves multiple molecular pathways to disrupt insulin signaling and is associa...
Oculomics uses the eye to gain insights into systemic health. Although initially focused on retinal imaging, oculomics now studies various ocular sign...
Alzheimer's disease (AD) is the most common type of dementia, accounting for at least two-thirds of dementia cases in people aged 65 and older. Numero...
Machine learning techniques have recently shown significant promise in electroencephalograph (EEG)-based depression recognition. However, existing met...
Oxidative stress is a central pathogenic process in the earliest stages of Alzheimer's disease (AD), promoting non-enzymatic protein modifications tha...
Neural vision restoration is a rapidly advancing discipline at the intersection of neuroscience, bioengineering, and ophthalmology. This review synthe...