Assessing kidney stone composition using smartphone microscopy and deep neural networks

OBJECTIVES: To propose a point‐of‐care image recognition system for kidney stone composition classification using smartphone microscopy and deep convolutional neural networks. MATERIALS AND METHODS: A total of 37 surgically extracted human kidney stones consisting of calcium oxalate (CaOx), cystine,...

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Detalles Bibliográficos
Autores principales: Onal, Ege Gungor, Tekgul, Hakan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
To the Future
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231678/
https://www.ncbi.nlm.nih.gov/pubmed/35783589
http://dx.doi.org/10.1002/bco2.137

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231678/
https://www.ncbi.nlm.nih.gov/pubmed/35783589
http://dx.doi.org/10.1002/bco2.137

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