A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning-Reference-Cited by-同舟云学术

A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning

Published:2021-10 Issue:4 Volume:100 Page:870-880
ISSN:0085-2538
Container-title:Kidney International
language:en
Short-container-title:Kidney International

Author:

Zheng Junjiong,Yu Hao,Batur Jesur,Shi Zhenfeng,Tuerxun Aierken,Abulajiang Abudukeyoumu,Lu Sihong,Kong Jianqiu,Huang Lifang,Wu Shaoxu,Wu Zhuo,Qiu Ya,Lin Tianxin,Zou Xiaoguang

Funder

National Natural Science Foundation of China

Science and Technology Planning Project of Guangdong Province

Publisher

Elsevier BV

Subject

Nephrology

Reference40 articles.

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4. Medical management of renal stones;Morgan;BMJ,2016

5. Derivation and validation of genome-wide polygenic score for urinary tract stone diagnosis;Paranjpe;Kidney Int,2020

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