The authors reply

Affiliations

¹ Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
² Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
³ Department of Pharmacy, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China.
⁴ Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. Electronic address: lintx@mail.sysu.edu.cn.

Comment

Junjiong Zheng et al. Kidney Int. 2021 Nov.

. 2021 Nov;100(5):1142-1143.

doi: 10.1016/j.kint.2021.08.009.

¹ Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
² Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China.
³ Department of Pharmacy, the First People's Hospital of Kashi Prefecture, Affiliated Kashi Hospital of Sun Yat-Sen University, Kashi, People's Republic of China.
⁴ Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People's Republic of China. Electronic address: lintx@mail.sysu.edu.cn.

No abstract available

Comment on

A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.
Zheng J, Yu H, Batur J, Shi Z, Tuerxun A, Abulajiang A, Lu S, Kong J, Huang L, Wu S, Wu Z, Qiu Y, Lin T, Zou X. Zheng J, et al. Kidney Int. 2021 Oct;100(4):870-880. doi: 10.1016/j.kint.2021.05.031. Epub 2021 Jun 12. Kidney Int. 2021. PMID: 34129883
A machine learning-based radiomic model for predicting urinary infection stone.
Zhang L, Zhang B. Zhang L, et al. Kidney Int. 2021 Nov;100(5):1142. doi: 10.1016/j.kint.2021.06.042. Kidney Int. 2021. PMID: 34688379 No abstract available.