Artificial Intelligence on Molecular Imaging to Predict the Risks of Parkinson's Disease for Patients With Rapid Eye Movement Sleep Behavior Disorder
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Enrollment
- 20
- Locations
- 1
- Primary Endpoint
- Assessment of Deep Learning Model Accuracy in Predicting Neurodegenerative Conversion in isolated REM sleep behavior disorder (iRBD) through Early Biomarker Detection
Study Overview
Brief Summary
The study aims to systematically document the course of REM sleep behavior disorder (RBD) and investigate possible clinical and imaging biomarkers for disease progression and conversion risk to Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). The study will use artificial intelligence to analyze imaging and develop a reliable method to predict and stratify patients approaching conversion to overt a-synucleinopathy. Participants will be clinically evaluated and 2 imaging procedures will be done.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Diagnostic
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to — (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
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
Arms & Interventions
NUK-RB Study
Intervention: PET/CT with 18-FDG (Device)
NUK-RB Study
Intervention: SPECT : 123 I-FP-CIT (DATSCAN) (Device)
NUK-RB Study
Intervention: MRI (Device)
Outcomes
Primary Outcomes
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
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.
Secondary Outcomes
- Evaluation of Deep Learning Model Accuracy in Predicting Conversion of Isolated REM Sleep Behavior Disorder (iRBD) to Parkinson's Disease(From enrollment to end of follow-up period, expected to be 48 months)
- Comparison of the Estimated versus Observed Annual Conversion Risk of Isolated Rapid Eye Movement Behavior Disorder (iRBD) to Neurodegenerative Disorders(From enrollment to end of follow-up period, expected to be 48 months)