MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study

BACKGROUND: To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS: A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assign...

Descripción completa

Detalles Bibliográficos
Autores principales: Mo, Xiaokai, Chen, Wenbo, Chen, Simin, Chen, Zhuozhi, Guo, Yuanshu, Chen, Yulian, Wu, Xuewei, Zhang, Lu, Chen, Qiuying, Jin, Zhe, Li, Minmin, Chen, Luyan, You, Jingjing, Xiong, Zhiyuan, Zhang, Bin, Zhang, Shuixing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2023
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902579/
https://www.ncbi.nlm.nih.gov/pubmed/36746892
http://dx.doi.org/10.1186/s13244-023-01370-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9902579/
https://www.ncbi.nlm.nih.gov/pubmed/36746892
http://dx.doi.org/10.1186/s13244-023-01370-4

Ejemplares similares