Wang W, Fan J, Huang G, Li J, Zhu X, Tian Y, Su L (2017) Prevalence of kidney stones in mainland China: a systematic review. Sci Rep 7:41630–41639. https://doi.org/10.1038/srep41630
Article CAS PubMed PubMed Central Google Scholar
Tan S, Yuan D, Su H, Chen W, Zhu S, Yan B, Sun F, Jiang K, Zhu J (2024) Prevalence of urolithiasis in China: a systematic review and meta-analysis. BJU Int 133(1):34–43. https://doi.org/10.1111/bju.16179
Article CAS PubMed Google Scholar
Zhu W, Liu Y, Lan Y, Li X, Luo L, Duan X, Lei M, Liu G, Yang Z, Mai X, Sun Y, Wang L, Lu S, Ou L, Wu W, Mai Z, Zhong D, Cai C, Zhao Z, Zeng G (2019) Dietary vinegar prevents kidney stone recurrence via epigenetic regulations. EBioMedicine 45:231–250. https://doi.org/10.1016/j.ebiom.2019.06.004
Article PubMed PubMed Central Google Scholar
Skolarikos A, Somani B, Neisius A, Jung H, Petřík A, Tailly T, Davis N, Tzelves L, Geraghty R, Lombardo R, Bezuidenhout C, Gambaro G (2024) Metabolic evaluation and recurrence prevention for urinary stone patients: an EAU guidelines update. Eur Urol 86(4):343–363. https://doi.org/10.1016/j.eururo.2024.05.029
Article CAS PubMed Google Scholar
Li J, Du Y, Huang G, Huang Y, Xi X, Ye Z (2025) Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values. Sci Rep 15(1):4327–4340. https://doi.org/10.1038/s41598-025-88704-y
Article CAS PubMed PubMed Central Google Scholar
Geraghty RM, Wilson I, Olinger E, Cook P, Troup S, Kennedy D, Rogers A, Somani BK, Dhayat NA, Fuster DG, Sayer JA (2023) Routine urinary biochemistry does not accurately predict stone type nor recurrence in kidney stone formers: a multicentre, multimodel, externally validated machine-learning study. J Endourol 37(12):1295–1304. https://doi.org/10.1089/end.2023.0451
Thongprayoon C, Krambeck AE, Rule AD (2020) Determining the true burden of kidney stone disease. Nat Rev Nephrol 16(12):736–746. https://doi.org/10.1038/s41581-020-0320-7
Kranz J, Bartoletti R, Bruyère F, Cai T, Geerlings S, Köves B, Schubert S, Pilatz A, Veeratterapillay R, Wagenlehner FME, Bausch K, Devlies W, Horváth J, Leitner L, Mantica G, Mezei T, Smith EJ, Bonkat G (2024) European association of urology guidelines on urological infections: summary of the 2024 guidelines. Eur Urol 86(1):27–41. https://doi.org/10.1016/j.eururo.2024.03.035
Liu X, Xie Z, Zhang Y, Huang J, Kuang L, Li X, Li H, Zou Y, Xiang T, Yin N, Zhou X, Yu J (2024) Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study. Cardiovasc Diabetol 23(1):407–420. https://doi.org/10.1186/s12933-024-02503-9
Article PubMed PubMed Central Google Scholar
Hu C, Li L, Huang W, Wu T, Xu Q, Liu J, Hu B (2022) Interpretable machine learning for early prediction of prognosis in sepsis: a discovery and validation study. Infect Dis Ther 11(3):1117–1132. https://doi.org/10.1007/s40121-022-00628-6
Article PubMed PubMed Central Google Scholar
Sorokin I, Mamoulakis C, Miyazawa K, Rodgers A, Talati J, Lotan Y (2017) Epidemiology of stone disease across the world. World J Urol 35(9):1301–1320. https://doi.org/10.1007/s00345-017-2008-6
Moreira DM, Friedlander JI, Hartman C, Elsamra SE, Smith AD, Okeke Z (2013) Using 24-hour urinalysis to predict stone type. J Urol 190(6):2106–2111. https://doi.org/10.1016/j.juro.2013.05.115
Article CAS PubMed Google Scholar
Chmiel JA, Stuivenberg GA, Wong JFW, Nott L, Burton JP, Razvi H, Bjazevic J (2024) Predictive modeling of urinary stone composition using machine learning and clinical data: implications for treatment strategies and pathophysiological insights. J Endourol 38(8):778–787. https://doi.org/10.1089/end.2023.0446
Article CAS PubMed Google Scholar
Abin Abraham NL, Kavoussi, Wilson Sui (2022) Machine learning prediction of kidney stone composition using electronic health Record-Derived features. J Endourol 36(2):243–250. https://doi.org/10.1089/end.2021.0211
Article PubMed PubMed Central Google Scholar
Zhu G, Jin L, Guo Y, Sun L, Li S, Zhou F (2024) Establishment and application of a nomogram diagram for predicting calcium oxalate stones in patients with urinary tract stones. Urolithiasis 52(1):40–47. https://doi.org/10.1007/s00240-024-01542-x
Article CAS PubMed PubMed Central Google Scholar
Wu Y, Mo Q, Xie Y, Zhang J, Jiang S, Guan J, Qu C, Wu R, Mo C (2023) A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo. Urolithiasis 51(1):84–92. https://doi.org/10.1007/s00240-023-01457-z
Article CAS PubMed PubMed Central Google Scholar
Li J, Du Y, Huang G, Zhang C, Ye Z, Zhong J, Xi X, Huang Y (2024) Predictive value of machine learning model based on CT values for urinary tract infection stones. iScience 27(12):110843–110857. https://doi.org/10.1016/j.isci.2024.110843
Article PubMed PubMed Central Google Scholar
Chen H-W, Chen Y-C, Lee J-T, Yang FM, Kao C-Y, Chou Y-H, Chu T-Y, Juan Y-S, Wu W-J (2022) Prediction of the uric acid component in nephrolithiasis using simple clinical information about metabolic disorder and obesity: a machine learning-based model. Nutrients 14(9):1829–1938. https://doi.org/10.3390/nu14091829
Article CAS PubMed PubMed Central Google Scholar
Tsaturyan A, Bokova E, Bosshard P, Bonny O, Fuster DG, Roth B (2020) Oral chemolysis is an effective, non-invasive therapy for urinary stones suspected of uric acid content. Urolithiasis 48(6):501–507. https://doi.org/10.1007/s00240-020-01204-8
Article PubMed PubMed Central Google Scholar
Chen T, Qian B, Zou J, Luo P, Zou J, Li W, Chen Q, Zheng L (2023) Oxalate as a potent promoter of kidney stone formation. Front Med 10:1159616–1159629. https://doi.org/10.3389/fmed.2023.1159616
Khan SR, Canales BK, Dominguez-Gutierrez PR (2021) Randall’s plaque and calcium oxalate stone formation: role for immunity and inflammation. Nat Rev Nephrol 17(6):417–433. https://doi.org/10.1038/s41581-020-00392-1
Article CAS PubMed Google Scholar
Coe FL, Worcester EM, Evan AP (2016) Idiopathic hypercalciuria and formation of calcium renal stones. Nat Rev Nephrol 12(9):519–533. https://doi.org/10.1038/nrneph.2016.101
Article CAS PubMed PubMed Central Google Scholar
Gambaro G, Croppi E, Coe F, Lingeman J, Moe O, Worcester E, Buchholz N, Bushinsky D, Curhan GC, Ferraro PM, Fuster D, Goldfarb DS, Heilberg IP, Hess B, Lieske J, Marangella M, Milliner D, Preminger GM, Reis Santos JM, Consensus Conference Group (2016) …. Metabolic diagnosis and medical prevention of calcium nephrolithiasis and its systemic manifestations: a consensus statement. Journal of Nephrology, 29(6), 715–734. https://doi.org/10.1007/s40620-016-0329-y
Shee K, Liu AW, Chan C, Yang H, Sui W, Desai M, Ho S, Chi T, Stoller ML (2024) A novel machine-learning algorithm to predict stone recurrence with 24-hour urine data. J Endourol 38(8):809–816. https://doi.org/10.1089/end.2023.0457
Article CAS PubMed PubMed Central Google Scholar
Jendeberg J, Thunberg P, Popiolek M, Lidén M (2021) Single-energy CT predicts uric acid stones with accuracy comparable to dual-energy CT-prospective validation of a quantitative method. Eur Radiol 31(8):5980–5989. https://doi.org/10.1007/s00330-021-07713-3
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