Using artificial intelligence and radiomics to analyze imaging features of neurodegenerative diseases
Experiments on multiple datasets-including ADNI, PPMI, and ABIDE for imaging, and YouTubePD and PDVD for behavioral signals-demonstrate that our approach consistently outperforms existing baselines, achieving an F1 score of 88.90 on ADNI and 85.43 on PPMI. These results highlight the framework's effectiveness in capturing disease patterns across imaging and non-imaging modalities, supporting its potential for real-world neurodegenerative disease monitoring and diagnosis.
Authors
- Qixuan Sun
- Fang Wang (ORCID: https://orcid.org/0000-0001-7762-8902)
Institutions
- Beijing Forestry University (CN)
- Northwest University (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-11-06
- DOI
- https://doi.org/10.3389/fneur.2025.1624867
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00