A novel integrative multimodal classifier to enhance the diagnosis of Parkinson's disease.

ABSTRACT: Parkinson's disease (PD) is a complex, progressive neurodegenerative disorder with high heterogeneity, making early diagnosis difficult. Early detection and intervention are crucial for slowing PD progression. Understanding PD's diverse pathways and mechanisms is key to advancing knowledge. Recent advances in noninvasive imaging and multi-omics technologies have provided valuable insights into PD's underlying causes and biological processes. However, integrating these diverse data sources remains challenging, especially when deriving meaningful low-level features that can serve as diagnostic indicators. This study developed and validated a novel integrative, multimodal predictive model for detecting PD based on features derived from multimodal data, including hematological information, pro

SUBMITTER: Zhou X 

PROVIDER: S-EPMC11891661 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature