Staging and quantification of florbetaben PET images using machine learning: impact of predicted regional cortical tracer uptake and amyloid stage on clinical outcomes

PURPOSE: We developed a machine learning–based classifier for in vivo amyloid positron emission tomography (PET) staging, quantified cortical uptake of the PET tracer by using a machine learning method, and investigated the impact of these amyloid PET parameters on clinical and structural outcomes....

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Detalles Bibliográficos
Autores principales: Kim, Jun Pyo, Kim, Jeonghun, Kim, Yeshin, Moon, Seung Hwan, Park, Yu Hyun, Yoo, Sole, Jang, Hyemin, Kim, Hee Jin, Na, Duk L., Seo, Sang Won, Seong, Joon-Kyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2019
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299909/
https://www.ncbi.nlm.nih.gov/pubmed/31884562
http://dx.doi.org/10.1007/s00259-019-04663-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299909/
https://www.ncbi.nlm.nih.gov/pubmed/31884562
http://dx.doi.org/10.1007/s00259-019-04663-3

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