Development and external validation of a machine learning-based model to classify uric acid stones in patients with kidney stones of Hounsfield units < 800
Author:
Funder
Yonsei University College of Medicine
Publisher
Springer Science and Business Media LLC
Subject
Urology
Link
https://link.springer.com/content/pdf/10.1007/s00240-023-01490-y.pdf
Reference21 articles.
1. Geraghty RM, Davis NF, Tzelves L et al (2022) Best practice in interventional management of urolithiasis: an update from the European Association of urology guidelines panel for urolithiasis 2022. Eur Urol Focus. https://doi.org/10.1016/j.euf.2022.06.014
2. Quhal F, Seitz C (2021) Guideline of the guidelines: urolithiasis. Curr Opin Urol 31:125–129. https://doi.org/10.1097/MOU.0000000000000855
3. Turk C, Petrik A, Sarica K et al (2016) EAU guidelines on interventional treatment for urolithiasis. Eur Urol 69:475–482. https://doi.org/10.1016/j.eururo.2015.07.041
4. Trinchieri A, Montanari E (2018) Biochemical and dietary factors of uric acid stone formation. Urolithiasis 46:167–172. https://doi.org/10.1007/s00240-017-0965-2
5. Shekarriz B, Stoller ML (2002) Uric acid nephrolithiasis: current concepts and controversies. J Urol 168(4 Pt 1):1307–1314. https://doi.org/10.1097/00005392-200210010-00003
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