Machine-learning based prediction models for Parkinson’s disease and atypical Parkinsonian disorders from advanced neuroimaging

Machine-learning based prediction models for Parkinson’s disease and atypical Parkinsonian disorders from advanced neuroimaging - HCPark

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

DRKS

Registry ID

DRKS00034050

Enrollment

580

Registered

2025-02-10

Start date

2020-01-20

Completion date

Unknown

Last updated

2026-04-27

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Idiopathic Parkinson`s disease

Interventions

Group 1: Idiopathic Parkinson`s disease: Neurological Examination Neuropsychological Examination 55 minutes MRI Group 2: Progressive supranuclear palsy: Neurological Examination Neuropsychological Exa

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: Neurologically diagnosed idiopathic Parkinson's disease, an atypical parkinsonian syndrome, or diagnosed Wilson disease. Healthy subjects, matched for age and gender to atypical parkinsonian disease and Wilson disease patients.

Exclusion criteria

Exclusion criteria: MRI contraindication (e.g. metal implants, claustrophobia), other neuropsychiatric diseases (e.g. dementia etc.), pregnancy and breastfeeding.

Design outcomes

Primary

Measure	Time frame
Improving the diagnosis of patients with various movement disorders	—

Secondary

Measure	Time frame
Are different neuroimaging methods (e.g. resting-state fMRI; DTI) capable of differentiating between examined diseases?	—

Countries

Germany

Contacts

Public ContactJulian Caspers

Heinrich-Heine-Universität und Universitätsklinikum Düsseldorf; Institut für Diagnostische und Interventionelle Radiologie

julian.caspers@med.uni-duesseldorf.de+4902118100

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: May 1, 2026