Latest AI and machine learning research in neurology for healthcare professionals.
Accurate disease prognosis is essential for patient care but is often hindered by the lack of long-term data. This study explores deep learning training strategies that utilize large, accessible diagnostic datasets to pretrain models aimed at predicting future disease progression in knee osteoarthritis (OA), Alzheimer’s disease (AD), and breast cancer (BC). While diagnostic pretraining improves pr...
Fluid biomarkers are emerging as crucial markers for diagnosis and disease monitoring in neurology. Epilepsy remains an exception despite seizures being known to result in metabolic changes, inflammation, and altered brain protein levels. Insight into how seizures affect the blood proteome is greatly needed. This cross-sectional study used plasma samples from adults aged 18-50 with epilepsy recrui...
Accurate prognostication of mobility outcomes is essential to guide rehabilitation and manage patient expectations. The prognostic utility of neuroima...
Sleep electroencephalographic (EEG) microstructures are closely related to cognition and undergo age-dependent changes. However, their multidimensiona...
Diabetes, hypertension, and dyslipidemia are major risk factors for cardiovascular (CVD), cerebral, and renal diseases (RD). However, the underlying m...
Objective: Identifying obsessive-compulsive disorder (OCD) using brain data remains challenging. Resting-state electroencephalography (EEG) offers an ...
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system. Early detection of the prodromal phase could enable timely inte...
Parkinson’s disease lacks reliable early diagnostics and disease-modifying treatments. Blood-based biomarkers can facilitate early detection, symptom ...
Covert cerebrovascular disease (CCD), comprising covert brain infarction (CBI) and white matter disease (WMD), is common in older adults and linked to...
Parent/patient-reported datasets provide ready access to phenotypic data for monogenic neurodevelopmental disorders yet their concordance with clinica...
Interictal epileptiform discharges (IEDs) are reliable biomarkers in electroencephalograms for epilepsy. To automate IED detection, deep learning (DL)...
Standardized assessment of clinical quality measures from electronic health records (EHRs) is challenging because information is fragmented across str...
Prior research suggests that meditation may slow brain aging and reduce the risk of Alzheimer’s disease (AD). However, we lack research systematically...
Neuron loss is a key feature of neurodegenerative diseases often leading to brain atrophy detectable through magnetic resonance imaging (MRI). Various...
Amyotrophic Lateral Sclerosis (ALS) progressively impairs motor functions, making communication increasingly difficult for affected individuals. Howev...
Alzheimer’s Disease and Related Dementias (ADRD) affect millions worldwide and can begin over a decade before symptoms appear. ADRD are generally irre...
Spinal cord injury (SCI) remains a devastating neurological condition with high global incidence and minimal curative options. The pathobiology is mul...
This study aimed to develop and validate a system of specialized deep lightweight convolutional neural networks (CNN) to accurately detect specific ar...
Speech is a rich and non-invasive source of clinical information, potentially providing digital biomarkers for neurological disorders such as Parkinso...
Cognitive decline affects approximately 40% of Parkinson’s disease (PD) patients within 10 years of diagnosis, progressing to dementia in 80% of patie...