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Use of deep learning-based radiomics to differentiate Parkinson's disease patients from normal controls: a study based on [18F]FDG PET imaging.
Sun, Xiaoming; Ge, Jingjie; Li, Lanlan; Zhang, Qi; Lin, Wei; Chen, Yue; Wu, Ping; Yang, Likun; Zuo, Chuantao; Jiang, Jiehui.
Afiliação
  • Sun X; Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
  • Ge J; PET Center, Huashan Hospital, Fudan University, Shanghai, 200235, China.
  • Li L; Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
  • Zhang Q; Institute of Biomedical Engineering, School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, China.
  • Lin W; Department of Neurosurgery, 904 Hospital of PLA, Wuxi, China.
  • Chen Y; Department of Nuclear Medicine, Affiliated Hospital of Southwest Medical University, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, No. 25, Taiping St, Luzhou, Sichuan, People's Republic of China, 646000.
  • Wu P; PET Center, Huashan Hospital, Fudan University, Shanghai, 200235, China.
  • Yang L; Department of Neurosurgery, 904 Hospital of PLA, Wuxi, China.
  • Zuo C; PET Center, Huashan Hospital, Fudan University, Shanghai, 200235, China. zuochuantao@fudan.edu.cn.
  • Jiang J; National Center for Neurological Disorder, Shanghai, 200040, China. zuochuantao@fudan.edu.cn.
Eur Radiol ; 32(11): 8008-8018, 2022 Nov.
Article em En | MEDLINE | ID: mdl-35674825

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doença de Parkinson / Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doença de Parkinson / Aprendizado Profundo Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article