Cargando…
Using Deep Learning Radiomics to Distinguish Cognitively Normal Adults at Risk of Alzheimer’s Disease From Normal Control: An Exploratory Study Based on Structural MRI
OBJECTIVES: We proposed a novel deep learning radiomics (DLR) method to distinguish cognitively normal adults at risk of Alzheimer’s disease (AD) from normal control based on T1-weighted structural MRI images. METHODS: In this study, we selected MRI data from the Alzheimer’s Disease Neuroimaging Ini...
Autores principales: | Jiang, Jiehui, Zhang, Jieming, Li, Zhuoyuan, Li, Lanlan, Huang, Bingcang |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070098/ https://www.ncbi.nlm.nih.gov/pubmed/35530047 http://dx.doi.org/10.3389/fmed.2022.894726 |
Ejemplares similares
-
A Novel Deep Learning Radiomics Model to Discriminate AD, MCI and NC: An Exploratory Study Based on Tau PET Scans from ADNI †
por: Zhao, Yan, et al.
Publicado: (2022) -
Combining PET with MRI to improve predictions of progression from mild cognitive impairment to Alzheimer’s disease: an exploratory radiomic analysis study
por: Yang, Fan, et al.
Publicado: (2022) -
Use of a Sparse-Response Deep Belief Network and Extreme Learning Machine to Discriminate Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls Based on Amyloid PET/MRI Images
por: Zhou, Ping, et al.
Publicado: (2021) -
Development of a Machine Learning Model to Discriminate Mild Cognitive Impairment Subjects from Normal Controls in Community Screening
por: Jiang, Juanjuan, et al.
Publicado: (2022) -
Alzheimer’s disease, mild cognitive impairment, and normal aging distinguished by multi-modal parcellation and machine learning
por: Sheng, Jinhua, et al.
Publicado: (2020)