Multimodal Brain Imaging Combined with Advanced Data Models for Detection of Prodromal Parkinson’s Disease: A Scoping Review
Ibrahim Serag1, Ahmed Y. Azzam2, Amr K. Hassan3, Rehab Adel Diab4, Mohamed Diab5, Mahmoud Tarek Hefnawy6, Mohamed Ahmed Ali7, Ahmed Negida8
1Faculty of Medicine, Mansoura University, 2Faculty of Medicine, October 6 University, Giza, Egypt., 3University of California, Irvine, CA, USA., 4Faculty of Medicine, Al-Azhar university, Cairo, Egypt., 5Faculty of Medicine Alexandria University, Alexandria, Egypt., 6Faculty of Medicine Zagazig University, Egypt, 7Qena Faculty of Medicine, South Valley University, Qena, Egypt, 8Virginia Commonwealth University
Objective:

This scoping review aims to investigate and explore the current diagnostic modalities used to detect prodromal PD, focusing particularly on multimodal imaging analysis and AI-based approaches.

 

Background:

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. PD is diagnosed by a combination of motor symptoms including bradykinesia, resting tremors, rigidity and postural instability. Prodromal PD is the stage preceding the onset of classic motor symptoms of PD. The diagnosis of prodromal PD remains challenging despite many available diagnostic modalities.

Design/Methods:

We adhered to the PRISMA-SR guidelines for scoping reviews. We conducted a comprehensive literature search at multiple databases such as PubMed, Scopus, Web of Science, and the Cochrane Library from inception to July 2024, using keywords related to prodromal PD and diagnostic modalities. We included studies based on predefined inclusion and exclusion criteria and performed data extraction using a standardized form.

Results:

The search included 9 studies involving 567 patients with prodromal PD and 35643 control. Studies utilized various diagnostic approaches including neuroimaging techniques and AI-driven models. sensitivity ranging from 43% to 84% and specificity up to 96%. Neuroimaging and AI technologies showed promising results in identifying early pathological changes and predicting PD onset. Highest specificity was achieved by neuromelanin-sensitive imaging model, while highest sensitivity was achieved by standard 10-s electrocardiogram (ECG)+Machine learning model

Conclusions:

Advanced diagnostic modalities such as AI-driven models and multimodal neuroimaging revealed promising results in early detection of prodromal PD. However, their clinical application as screening tool for prodromal PD is limited because of the lack of validation. Future research should be directed towards using Multimodal imaging in diagnosing and screening for prodromal PD.

10.1212/WNL.0000000000208715
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