Neurology

ML-Based Multidomain Speech Biomarkers for Multiclass Parkinson's Severity: An Indian Corpus and Clinically Calibrated Labels.

OBJECTIVE: Speech provides a lightweight window into articulatory and phonatory impairment in Parkinson's Disease (PD), yet clinically reliable severity staging has to extend across speakers. This study develops a PD severity classification framework which compares Subject Dependent (SD) and subject-independent (SI) settings on clinically calibrated labels such as mild, severe, and Healthy Control...

AI-Driven Speech Analysis for Mental Health Prediction in Individuals with Voice Disorders.

BACKGROUND AND OBJECTIVE: Mental health disorders are common among individuals with voice disorders, yet applications of AI-driven speech analysis in this population remain limited. It is also unclear whether the same acoustic features contribute to mental health prediction across different etiologies. We aimed to develop an interpretable, fairness-aware artificial intelligence and machine learnin...

Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.

OBJECTIVES: Neuropsychological (NP) tests are multi-domain in execution. Reliance on a single score representing specific domains obscures the detecti...

Integrated multi-omics and machine learning identify Casp8 and Pycard as central regulators of pyroptosis and necroptosis in spinal cord injury.

Programmed cell death pathways exacerbate secondary damage after spinal cord injury, yet their shared regulators and tractable therapeutic points rema...

Digital Technology for Sleep Symptoms in Parkinson's Disease: A Scoping Review.

Sleep disturbances are highly prevalent and clinically significant non-motor features of Parkinson's disease (PD). Although in-laboratory polysomnogra...

Fully Automatic Diabetic Wound Segmentation Using Lightweight Deep Convolutional Neural Networks.

Diabetic foot ulcers, resulting from neuropathic and/or vascular complications in patients with diabetes mellitus, pose a major global health challeng...

Alzheimer's and Parkinson's Detection with Video-Based Hybrid Deep Learning from Brain MRI.

Dementia, which refers to disorders related to human memory, significantly affects the human brain, and a person with it can experience certain diffic...

Artificial Intelligence in Spine Imaging Interpretation.

Spinal disorders, one of the leading causes of disability worldwide, are routinely assessed on imaging studies. Recent advancements in artificial inte...

Large-scale brain network alteration among OCD patients with suicidal thoughts and behavior: A microstate analysis of the electroencephalogram.

BACKGROUND: The prevalence of suicidality in obsessive-compulsive disorder (OCD) is understudied. Moreover, identifying neurobiological markers of sui...

Deep learning magnetic resonance imaging algorithm for differentiating metastatic vertebral fractures.

BACKGROUND CONTEXT: Distinguishing malignant metastatic lesions from benign osteoporotic vertebral compression fractures (VCFs) is a major diagnostic ...

Dual Component Framework Approach for Automated Nerve Conduction Study Interpretation: Rule-Based Coding and Fine-Tuned Language Model.

INTRODUCTION/AIMS: Nerve conduction study (NCS) interpretation is labor-intensive, and automation may improve clinical workflow. We developed a dual f...

Automated Imaging Differentiation for Dementia: Including Alzheimer Disease Dementia and Dementia with Lewy Bodies.

BACKGROUND AND OBJECTIVES: Differentiation of Alzheimer's disease dementia (ADD) and dementia with Lewy bodies (DLB) remains a challenge. Free-water i...

Machine Learning Identifies TRAPPC13/COPS5 as Biomarkers and Vesicle Transport Subtypes in Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disorder characterized by neuron loss and abnormal protein trafficking. Dysregulation of v...

Computational renaissance in herbal medicine: A 20-year bibliometric deciphering of molecular dynamics simulations reshaping traditional Chinese medicine drug discovery.

Molecular dynamics (MD) simulations have emerged as pivotal tools for deciphering the molecular mechanisms of Traditional Chinese Medicine (TCM), yet ...

E2T: EEG-to-Trajectory Transformer for Motor Imagery-based Fully-DoF Motion Prediction.

Brain-computer interfaces (BCIs) using electroen-cephalography (EEG) enable non-invasive, real-time interaction for individuals with motor impairments...

Performance of Machine Learning Models for Sepsis and Stroke Detection Using EMS Data.

OBJECTIVES: Early sepsis and stroke recognition by emergency medical services (EMS) improves triage, treatment, and patient outcomes. Machine learning...

Using Machine Learning to Improve Screening for Oropharyngeal Dysphagia in Hospitalized Versus Primary Care Adult Patients With COVID-19 Disease: Retrospective Observational Study.

BACKGROUND: Oropharyngeal dysphagia (OD) commonly occurs in patients with COVID-19 disease, posing diagnostic challenges due to isolation protocols. O...

Development of a machine learning-based survival prediction model for ALS inclusive of the advanced-stage population.

OBJECTIVE: Develop a machine learning-based model for survival prediction in ALS, including advanced-stage patients (≤50% predicted normal vital capac...

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...

Confounding factors in the generalisation of synthetic CT to diagnostic spine MRI.

Generalisation of synthetic CT (sCT) generation to diagnostic MRI data in the spine faces many challenges, particularly when aiming at accurate visual...