Parkinson's Disease

A coarse-to-fine machine-learning framework for identifying functional connectivity markers of cognitive impairment in Parkinson's disease.

BACKGROUND: Resting-state functional MRI (rs-fMRI) has been applied to investigate cognitive impairment (CI) in Parkinson's disease (PD). Nevertheless, reported functional connectivity (FC) alterations remain heterogeneous, partly due to reliance on linear analytical approaches and limited validation across datasets. OBJECTIVE: To develop a machine-learning framework for identifying generalizable ...

Semi-supervised ensemble learning with interval type-2 fuzzy-rough sets for Parkinson's disease prediction from multi-omics.

Parkinson's disease (PD) is a disorder involving progressive degeneration of the nervous system. Its clinical signs typically become noticeable only after substantial impairment has occurred in the substantia nigra, a brain region involved in motor control. Therefore, early and accurate prediction of PD based on molecular alterations is essential for proper diagnosis and improved patient outcomes....

Technology-enabled telerehabilitation for Parkinson's disease: a scoping review of digital rehabilitation systems, delivery architectures, and implementation challenges.

INTRODUCTION: Digital technologies are increasingly integrated into neurorehabilitation programs for Parkinson's Disease (PD), enabling remote deliver...

Detection of early-stage Parkinson's disease using wearable sensors at multiple body locations and convolutional neural networks.

Early-stage Parkinson's disease (PD) presents with subtle motor symptoms that complicate timely diagnosis. We developed a non-invasive detection frame...

The role of AI-assisted drug repurposing in neurological disorders: a systematic review of validation strategies, challenges and opportunities.

Neurological disorders refer to a diverse group of conditions that affect the brain, peripheral nerves, and spinal cord and impair socioemotional, cog...

Integrated multi-omics analysis identifies key microglial subpopulations and therapeutic targets in Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is a rapidly growing global health concern, with aging populations driving increasing prevalence. While neuronal ...

Actigraphy Meets AI: A Digital Biomarker for Parkinson's Disease and Isolated REM Sleep Behaviour Disorder.

Isolated rapid eye movement (REM) sleep behaviour disorder (RBD) is an early stage of alpha-synucleinopathies, such as Parkinson's disease (PD). Actig...

Bridging the global Parkinson's divide: Technology as a structural solution for equitable and brain health-integrated care.

Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder worldwide, with projections exceeding 25 million people by 2050. Its burden...

Cross Domain Self-Prompting SAM2 for Intraoperative OCT Video Segmentation.

Retinal procedures require precise instrument manipulation to avoid iatrogenic injuries. Intraoperative Optical Coherence Tomography (iOCT) delivers r...

3D OCT-Based Retinal Biomarker Analysis for Automatic Regional-Wise Characterization of Neurodegenerative Diseases.

Neurodegenerative diseases (NDDs) such as Alzheimer's disease (AD), essential tremor (ET), multiple sclerosis (MS), and Parkinson's disease (PD) are c...

Generalizable and explainable deep learning for brain MRI: a multi-cohort evaluation of 3D architectures for age and sex prediction.

Deep learning (DL) methods increasingly outperform classical approaches in brain MRI analysis, yet their generalizability across independent imaging c...

Structure aware graph community cluster pruning for efficient neural network compression in Parkinson's disease diagnosis.

Deep neural networks in medical and edge environments often face computational and memory constraints, which necessitate effective model compression. ...

Semiquantitative [¹²³I]FP-CIT SPECT metrics combined with machine learning improve clinical differentiation of Parkinson's disease and atypical parkinsonian syndrome.

PURPOSE: To evaluate whether semiquantitative striatal [¹²³I]FP-CIT SPECT-derived metrics improve clinical differentiation of degenerative parkinsonis...

Identifying unique gait phenotypes across neuromotor conditions using wearable inertial sensors and topological data analysis.

Wearable sensors quantify gait and mobility in detail, but translating high-dimensional data into clinically actionable insights remains challenging i...

Variations of global brain asymmetry are associated with aging and related diseases.

Lateralization is a hallmark of brain organization, yet the structural basis underlying this phenomenon remains a critical, unresolved question in cog...

Intelligent differentiation between Parkinson's disease and essential tremor using wearable sensors and machine learning: a temporal validation study.

BACKGROUND: Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders with overlapping clinical presentations but divergent ...

Parkinson's disease classification using optimized attention-based deep learning from EEG signals with interpretable sub-band topography.

Parkinson's disease (PD) diagnosis remains challenging because subtle neural alterations may be difficult to capture using conventional clinical asses...

Multi-omics approaches to parkinsonism: genomic, proteomic, and non-coding RNA perspectives.

Parkinson's disease (PD) is a progressive neurodegenerative disorder with significant variability associated with substantial loss of dopaminergic neu...

An improved catch fish optimization based deep learning model for Parkinson disease classification using EEG signal.

Accurate and objective identification of Parkinson's Disease (PD) from Electroencephalogram (EEG) signals is important because EEG responses are compl...

A Robust Voice-Based Parkinson's Disease Diagnosis Model via Semantic RGB Feature Transformation and Hybrid Deep Learning.

Reliable diagnosis of Parkinson's disease (PD) in its early stages is of vital importance for supporting timely clinical decision-making and improving...