Assessment of glomerular morphological patterns by deep learning algorithms

BACKGROUND: Compilation of different morphological lesion signatures is characteristic of renal pathology. Previous studies have documented the potential value of artificial intelligence (AI) in recognizing relatively clear-cut glomerular structures and patterns, such as segmental or global sclerosi...

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Detalles Bibliográficos
Autores principales: Weis, Cleo-Aron, Bindzus, Jan Niklas, Voigt, Jonas, Runz, Marlen, Hertjens, Svetlana, Gaida, Matthias M., Popovic, Zoran V., Porubsky, Stefan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927010/
https://www.ncbi.nlm.nih.gov/pubmed/34982414
http://dx.doi.org/10.1007/s40620-021-01221-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927010/
https://www.ncbi.nlm.nih.gov/pubmed/34982414
http://dx.doi.org/10.1007/s40620-021-01221-9

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