The Detection of Invisible Abnormal Metabolism in the FDG-PET Images of Patients With Anti-LGI1 Encephalitis by Machine Learning

OBJECTIVE: This study aims to detect the invisible metabolic abnormality in PET images of patients with anti-leucine-rich glioma-inactivated 1 (LGI1) encephalitis using a multivariate cross-classification method. METHODS: Participants were divided into two groups, namely, the training cohort and the...

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Detalles Bibliográficos
Autores principales: Pan, Jian, Lv, Ruijuan, Zhou, Guifei, Si, Run, Wang, Qun, Zhao, Xiaobin, Liu, Jiangang, Ai, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Neurology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197115/
https://www.ncbi.nlm.nih.gov/pubmed/35711267
http://dx.doi.org/10.3389/fneur.2022.812439

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9197115/
https://www.ncbi.nlm.nih.gov/pubmed/35711267
http://dx.doi.org/10.3389/fneur.2022.812439

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