Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Using discrete- and continuous-time machine learning models (Nnet, CoxNet, GLMnet) to explore sex and age differences in stroke prediction among hypertensive individuals

Stroke is one of the leading causes of death and long-term disability globally. Several studies have investigated the incidence and predictors of stroke in the healthy population; only a few have specifically focused on stroke risk prediction among individuals with hypertension. Given that hypertension is the most common modifiable risk factor for stroke, this represents an important research area...

Automatic screening and characterization of patients with acquired neurological conditions from language

Individuals with left-hemisphere damage (LHD), right-hemisphere damage (RHD), dementia, mild cognitive impairment (MCI), traumatic brain injury (TBI), and healthy controls are characterized by overlapping clinical profiles affecting communication and social interaction. Language provides a rich, non-invasive window into neurological health, yet objective and scalable methods to automatically diffe...

Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with a largely unknown duration and pathophysiology of the pre-diagnostic ph...

Data-driven modelling of tau pathology reveals distinct progressive supranuclear palsy subtypes

Progressive supranuclear palsy (PSP) is a heterogeneous neurodegenerative disease characterised by the accumulation of misfolded 4-repeat tau within n...

Postmortem Validation of Quantitative MRI for White Matter Hyperintensities in Alzheimer’s Disease

White matter hyperintensities (WMH) are frequently observed on MRI in aging and Alzheimer’s disease (AD), yet their microstructural pathology remains ...

BrainSignsNET: Deep Learning-Based 3D Anatomical Landmark Detection in Human Brain Imaging

Accurate detection of anatomical landmarks in brain Magnetic Resonance Imaging (MRI) scans is essential for reliable spatial normalization, image alig...

Automated Deep Learning Pipeline for Callosal Angle Quantification

Normal pressure hydrocephalus (NPH) is a potentially treatable neurodegenerative disorder that remains underdiagnosed due to its clinical overlap with...

Artificial intelligence applications for dementia: A systematic review for clinical research

Artificial intelligence and emerging technologies are driving a transformative shift in society, particularly in the healthcare sector, where they enh...

PABformer: A Multi-Channel Transformer for Physical Activity Behavior Representation and Health Outcome Assessment Using UK Biobank Accelerometer Data

Physical activity (PA) is a critical, modifiable determinant of health. The relationship between PA behavior and health outcomes has been increasingly...

Decoding phantom limb movements from intraneural recordings

Limb loss leads to severe sensorimotor deficits and requires the use of a prosthetic device, especially in lower-limb amputees. While direct recording...

Machine Learning-Based Reconstruction of 2D MRI for Quantitative Morphometry in Epilepsy

Structural neuroimaging analyses require ‘research quality’ images, acquired with costly MRI acquisitions. Isotropic (3D-T1) images are desirable for ...

Identifying Sex-Specific Sub-phenotypes of Alzheimer’s Disease Progression Using Longitudinal Electronic Health Records

Alzheimer’s Disease (AD) is a complex neurodegenerative disorder strongly influenced by sex differences, with women comprising nearly two-thirds of ca...

Integrating Machine Learning Pipelines for Multimodal Biomarker Prediction in Alzheimer’s and Parkinson’s Disease: A Component of the Neurodiagnoses Framework

Alzheimer’s and Parkinson’s diseases are age-related neurodegenerative diseases that often require invasive procedures for diagnosis. Traditional diag...

Real-world federated learning for the brain imaging scientist

Federated learning (FL) could boost deep learning in neuroimaging but is rarely deployed in a real-world scenario, where its true potential lies. Here...

Accelerometer-measured weekend catch-up sleep and incident dementia: a prospective cohort study

To investigate whether accelerometer-measured weekend catch-up sleep, defined as extending sleep on weekends to compensate for weekday sleep inadequac...

A deep learning algorithm based on fundus photographs to measure retinal vascular parameters and their additional value beyond the CAIDE risk score for predicting 14-year dementia risk

Retinal photography is a valuable non-invasive tool for assessing the nature of vessel changes. It is of interest whether retinal vascular parameters ...

Multi-organ AI Endophenotypes Chart the Heterogeneity of Pan-disease in the Brain, Eye, and Heart

Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease ...

Toward Non-Invasive Voice Restoration: A Deep Learning Approach Using Real-Time MRI

Despite recent advances in brain–computer interfaces (BCIs) for speech restoration, existing systems remain invasive, costly, and inaccessible to indi...

Early Subtypes and Progressions of Progressive Supranuclear Palsy: A Data-Driven Brain Bank Study

Progressive supranuclear palsy (PSP) is typically characterized by vertical supranuclear gaze palsy and early falls, referred to as Richardson’s syndr...

Clinically meaningful combined improvements of sleep, physical activity, and nutrition (SPAN) in relation to major adverse cardiovascular events

Sleep, physical activity, and nutrition (SPAN) are major modifiable risk factors for cardiovascular disease, yet the minimum and optimal combined impr...

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