Artificial Intelligence in Molecular Imaging: Predicting Parkinson's Risk in REM Sleep Behavior Disorder (NUK-RBD)
Artificial Intelligence on Molecular Imaging to Predict the Risks of Parkinson's Disease for Patients With Rapid Eye Movement Sleep Behavior Disorder
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Phase
Phase
- Not Applicable
Contacts and Locations
Study Contact
Study Contact
- Name: Axel Rominger, Prof. Dr. med.
- Phone Number: +41 316322610
- Email: axel.rominger@insel.ch
Study Contact Backup
- Name: Franziska Strunz, PhD
- Phone Number: +41 316643022
- Email: studies.nuk@insel.ch
Study Locations
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Bern, Switzerland, 3010
- Recruiting
- Inselspital, University Clinic for Nuclear Medicine
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Contact:
- Axel Rominger, Prof. Dr. med.
- Phone Number: +41 31 632 26 10
- Email: axel.rominger@insel.ch
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Contact:
- Franziska Strunz, PhD
- Phone Number: +41 31 66 4 30 22
- Email: studies.nuk@insel.ch
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Description
Inclusion Criteria:
- Confirmed clinical iRBD diagnosis by movement disorder specialists according to the International Classification of Sleep Disorders
- Written informed consent
Exclusion Criteria:
- Known diagnosis of PD or other neurodegenerative disorder
- Unequivocal signs of parkinsonism on examination
- Narcolepsy or other known causes of RBD
- Moderate to severe obstructive sleep apnea
- Abnormal neurological or MRI examination
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: N/A
- Interventional Model: Single Group Assignment
- Masking: None (Open Label)
Number of Arms
Arms and Interventions
Participant Group / ArmParticipant Group / Arm |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Experimental: NUK-RB Study
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FDG-PET scans will be acquired in a Siemens Biograph Vision Quadra PET/CT (Siemens, Germany) at 30-minute post-injection of approximately 80 MBq 18F-FDG.
The duration of the acquisition is 20 minutes.
The PET images will be reconstructed with the vendor's time of flight (TOF) point-spread-function (PSF) algorithm, following corrections for randoms, scatter, and decay.
Attenuation correction will be performed first using low-dose CT.
DaT-Scans will be acquired in a GE Discovery NM/CT 670 Pro™.
After injection of approximately 110 MBq 123I-FP-CIT, images will be acquired within 4 h post-injection.
The duration of the acquisition is 35 minutes.
MRI examination to exclude structural brain anomalies.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Assessment of Deep Learning Model Accuracy in Predicting Neurodegenerative Conversion in isolated REM sleep behavior disorder (iRBD) through Early Biomarker Detection
Time Frame: From enrollment to end of follow-up period, expected to be 48 months
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The investigators aim to evaluate the predictive accuracy of a deep learning model in identifying patients with iRBD who will progress to a neurodegenerative disorder.
The primary outcome will assess the model's sensitivity in detecting early imaging biomarkers linked to disease progression, with the goal of enabling earlier intervention and improving long-term outcomes.
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From enrollment to end of follow-up period, expected to be 48 months
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Comparison of the Estimated versus Observed Annual Conversion Risk of Isolated Rapid Eye Movement Behavior Disorder (iRBD) to Neurodegenerative Disorders
Time Frame: From enrollment to end of follow-up period, expected to be 48 months
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The investigators aim to compare the estimated annual conversion risk of 6.3% in patients with iRBD to Parkinson's disease or another overt alpha-synucleinopathy with the conversion rates observed in the study.
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From enrollment to end of follow-up period, expected to be 48 months
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Evaluation of Deep Learning Model Accuracy in Predicting Conversion of Isolated REM Sleep Behavior Disorder (iRBD) to Parkinson's Disease
Time Frame: From enrollment to end of follow-up period, expected to be 48 months
|
The investigators aim to evaluate the accuracy, receiver operating characteristic curves and area under the curve, specificity, and positive and negative predictive values of the applied deep learning method, predicting the conversion risk from iRBD to Parkinson's disease or another overt alpha-synucleinopathy.
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From enrollment to end of follow-up period, expected to be 48 months
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Principal Investigator: Kuanggyu Shi, Prof. Dr. ing., University Bern, Inselspital, Center for Artificial Intelligence in Medicine
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Estimated)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Synucleinopathies
- Brain Diseases
- Central Nervous System Diseases
- Nervous System Diseases
- Neurocognitive Disorders
- Dementia
- Neurodegenerative Diseases
- Sleep Wake Disorders
- Movement Disorders
- Parkinsonian Disorders
- Basal Ganglia Diseases
- Parasomnias
- REM Sleep Parasomnias
- Mental Disorders
- Parkinson Disease
- Lewy Body Disease
- REM Sleep Behavior Disorder
- Molecular Mechanisms of Pharmacological Action
- Radiopharmaceuticals
- Fluorodeoxyglucose F18
Other Study ID Numbers
Other Study ID Numbers
- 2023-00816
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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