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Deep learning is a promising technology and seems to be the future of the CT stone evaluation

Detalles Bibliográficos
Autor principal: Danilovic, Alexandre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Sociedade Brasileira de Urologia 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388183/
https://www.ncbi.nlm.nih.gov/pubmed/35838510
http://dx.doi.org/10.1590/S1677-5538.IBJU.2022.0132.1
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author Danilovic, Alexandre
author_facet Danilovic, Alexandre
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spelling pubmed-93881832022-08-21 Deep learning is a promising technology and seems to be the future of the CT stone evaluation Danilovic, Alexandre Int Braz J Urol Editorial Comment Sociedade Brasileira de Urologia 2022-08-03 /pmc/articles/PMC9388183/ /pubmed/35838510 http://dx.doi.org/10.1590/S1677-5538.IBJU.2022.0132.1 Text en https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Editorial Comment
Danilovic, Alexandre
Deep learning is a promising technology and seems to be the future of the CT stone evaluation
title Deep learning is a promising technology and seems to be the future of the CT stone evaluation
title_full Deep learning is a promising technology and seems to be the future of the CT stone evaluation
title_fullStr Deep learning is a promising technology and seems to be the future of the CT stone evaluation
title_full_unstemmed Deep learning is a promising technology and seems to be the future of the CT stone evaluation
title_short Deep learning is a promising technology and seems to be the future of the CT stone evaluation
title_sort deep learning is a promising technology and seems to be the future of the ct stone evaluation
topic Editorial Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9388183/
https://www.ncbi.nlm.nih.gov/pubmed/35838510
http://dx.doi.org/10.1590/S1677-5538.IBJU.2022.0132.1
work_keys_str_mv AT danilovicalexandre deeplearningisapromisingtechnologyandseemstobethefutureofthectstoneevaluation