MRI radiomics combined with machine learning for diagnosing mild cognitive impairment: a focus on the cerebellar gray and white matter
Radiomic features based on the cerebellar gray and white matter, combined with machine learning, can objectively diagnose MCI, which provides significant clinical value for assisted diagnosis.
Authors
- Yini Chen
- Zhinan Ye (ORCID: https://orcid.org/0009-0006-7301-1300)
- Yaping Zhang (ORCID: https://orcid.org/0000-0003-3381-1486)
- Wenjie Wang (ORCID: https://orcid.org/0000-0002-5199-1428)
- Ying Chen (ORCID: https://orcid.org/0000-0001-6396-7815)
- Yi Chen (ORCID: https://orcid.org/0000-0002-0528-5776)
- Andong Lin
- Weili Luo
Institutions
- Dalian Medical University (CN)
- Taizhou Municipal Hospital (CN)
- Taizhou Central Hospital (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
- Taizhou University (CN)
Publication Details
- Journal
- Frontiers in Aging Neuroscience
- Published
- 2024-10-04
- DOI
- https://doi.org/10.3389/fnagi.2024.1460293
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.45
Funders
- U.S. Department of Defense
- Alzheimer's Association
- Bristol-Myers Squibb
- Eli Lilly and Company
- Pfizer
- Biogen
- University of Southern California
- Meso Scale Diagnostics
- Alzheimer's Disease Neuroimaging Initiative
- BioClinica
- Novartis Pharmaceuticals Corporation
- Northern California Institute for Research and Education
- Eisai
- Servier
- H. Lundbeck A/S
- IXICO
- National Institutes of Health
- Genentech
- Canadian Institutes of Health Research