AIMC Topic: Parkinson Disease

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Digital Biomarkers for Parkinson Disease: Bibliometric Analysis and a Scoping Review of Deep Learning for Freezing of Gait.

Journal of medical Internet research
BACKGROUND: With the rapid development of digital biomarkers in Parkinson disease (PD) research, it has become increasingly important to explore the current research trends and key areas of focus.

AI-DBS study: protocol for a longitudinal prospective observational cohort study of patients with Parkinson's disease for the development of neuronal fingerprints using artificial intelligence.

BMJ open
INTRODUCTION: Deep brain stimulation (DBS) is a proven effective treatment for Parkinson's disease (PD). However, titrating DBS stimulation parameters is a labourious process and requires frequent hospital visits. Additionally, its current applicatio...

Co-Designing a "win-win" in Predictive AI: First Results from Interviews and Focus Groups with Persons with Parkinson's Disease.

Studies in health technology and informatics
This study explored the perspectives of people with Parkinson's disease (PwP) involved in the co-design of AI tools for PD care. The aim was to understand PwP perspectives on AI tools and identify factors influencing their engagement. A qualitative t...

Impact of canny edge detection preprocessing on performance of machine learning models for Parkinson's disease classification.

Scientific reports
This study investigates the classification of individuals as healthy or at risk of Parkinson's disease using machine learning (ML) models, focusing on the impact of dataset size and preprocessing techniques on model performance. Four datasets are cre...

A Multi-Modal Graph Neural Network Framework for Parkinson's Disease Therapeutic Discovery.

International journal of molecular sciences
Parkinson's disease (PD) is a complex neurodegenerative disorder lacking effective disease-modifying treatments. In this study, we integrated large-scale protein-protein interaction networks with a multi-modal graph neural network (GNN) to identify a...

A new network structure for Parkinson's handwriting image recognition.

Medical engineering & physics
Parkinson's disease (PD) remains a condition without a cure, though its early manifestations can be managed effectively by medical professionals. This underscores the significance of early detection of PD. It has been widely demonstrated that handwri...

Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.

European journal of neurology
BACKGROUND: Postural instability and gait difficulties are key symptoms of Parkinson's disease (PD), elevating the risk of falls substantially. Falls afflict 35% to 90% of PD patients, representing a major challenge in managing the condition. Accurat...

Automated Imaging Differentiation for Parkinsonism.

JAMA neurology
IMPORTANCE: Magnetic resonance imaging (MRI) paired with appropriate disease-specific machine learning holds promise for the clinical differentiation of Parkinson disease (PD), multiple system atrophy (MSA) parkinsonian variant, and progressive supra...

AskBeacon-performing genomic data exchange and analytics with natural language.

Bioinformatics (Oxford, England)
MOTIVATION: Enabling clinicians and researchers to directly interact with global genomic data resources by removing technological barriers is vital for medical genomics. AskBeacon enables large language models (LLMs) to be applied to securely shared ...

Analysis of Two Neuroanatomical Subtypes of Parkinson's Disease and Their Motor Progression Based on Semi-Supervised Machine Learning.

CNS neuroscience & therapeutics
BACKGROUND: The high heterogeneity of Parkinson's disease (PD) hinders personalized interventions. Brain structure reflects damage and neuroplasticity and is one of the biological bases of symptomatology. Subtyping PD in the framework of brain struct...