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AI-based predictive biomarkers for chronic neurological diseases: the rAIdD prospective, multicenter, observational
Simone Varrasi1, Alfredo Pulvirenti2, Vincenzo Catania3
1Department of Medical, Surgical Sciences and Advanced Technologies "G. F. Ingrassia", University of Catania, Catania, Italy.
Frontiers in Neurology
|August 1, 2026
Summary
This study introduces the rAIdD project, using AI and digital tools to improve early diagnosis and management of chronic neurological disorders like Multiple Sclerosis (MS), Parkinson's disease (PD), and Alzheimer's Disease (AD). The goal is to create a digital infrastructure for better patient monitoring and risk stratification.
Area of Science:
- Neurology
- Digital Health
- Artificial Intelligence
Background:
- Chronic neurological disorders (Multiple Sclerosis, Parkinson's disease, Alzheimer's Disease) pose a significant global health challenge due to progressive neurodegeneration and disability.
- These diseases share complex pathophysiological mechanisms influenced by genetic, environmental, and lifestyle factors.
- Advancements in AI, wearable technology, and data integration offer new avenues for early detection and personalized management.
Purpose of the Study:
- To develop an interoperable digital infrastructure, the rAIdD project, for early diagnosis, monitoring, and risk stratification of chronic neurological diseases.
- To focus on the neurological component of the rAIdD network, specifically for Multiple Sclerosis (MS), Parkinson's disease (PD), and Alzheimer's Disease (AD).
Main Methods:
- A prospective, multicenter observational study involving 780 participants (300 MS, 150 PD, 150 AD, 180 controls) followed for 18 months.
- Utilizing standardized clinical, neuropsychological, neuroimaging, and continuous digital monitoring via wearable sensors for biometric and behavioral data.
- Integrating multimodal data (clinical, imaging, digital) on a centralized platform and applying machine learning for biomarker identification and disease progression modeling.
Main Results:
- N/A - This is a study protocol; results are pending.
Conclusions:
- N/A - This is a study protocol; conclusions will be drawn upon study completion.
