Prediction of the composition of urinary stones using deep learning

PURPOSE: This study aimed to predict the composition of urolithiasis using deep learning from urinary stone images. MATERIALS AND METHODS: We classified 1,332 stones into 31 classes according to the stone composition. The top 4 classes with a frequency of 110 or more (class 1: calcium oxalate monohy...

Descripción completa

Detalles Bibliográficos
Autores principales: Kim, Ui Seok, Kwon, Hyo Sang, Yang, Wonjong, Lee, Wonchul, Choi, Changil, Kim, Jong Keun, Lee, Seong Ho, Rim, Dohyoung, Han, Jun Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Urological Association 2022
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262483/
https://www.ncbi.nlm.nih.gov/pubmed/35670006
http://dx.doi.org/10.4111/icu.20220062

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9262483/
https://www.ncbi.nlm.nih.gov/pubmed/35670006
http://dx.doi.org/10.4111/icu.20220062

Ejemplares similares