EAU Guidelines (2023) Edn. presented at the EAU Annual Congress Milan. ISBN 978-94-92671-19-6
Dellabella M, Milanese G, Muzzonigro G (2003) Efficacy of tamsulosin in the medical management of juxtavesical ureteral stones. J Urol 170(6 Pt 1):2202–2205
Article CAS PubMed Google Scholar
Yoshida T et al (2019) Ureteral wall thickness as a significant factor in predicting spontaneous passage of ureteral stones of≤ 10 mm: a preliminary report. World J Urol 37:913–919
Yallappa S et al (2018) Natural history of conservatively managed ureteral stones: analysis of 6600 patients. J Endourol 32(5):371–379
Lane J et al (2020) Correlation of operative time with outcomes of ureteroscopy and stone treatment: a systematic review of literature. Curr Urol Rep 21(4):17
Heidenberg DJ et al (2023) Timing of ureteral stent removal after ureteroscopy on stent-related symptoms: a validated questionnaire comparison of 3 and 7 days stent duration. J Endourol 38:82
Geraghty RM et al (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
Li P et al (2023) Machine learning algorithms in predicting the recurrence of renal stones using clinical data. Urolithiasis 52(1):12
Article CAS PubMed Google Scholar
Chmiel JA et al (2023) Predictive modelling of urinary stone composition using machine learning and clinical data: implications for treatment strategies and pathophysiological insights. J Endourol. https://doi.org/10.1089/end.2023.0446
Abbod MF et al (2007) Application of artificial intelligence to the management of urological cancer. J Urol 178(4 Pt 1):1150–1156
Scott Wang HH, Vasdev R, Nelson CP (2024) Artificial intelligence in pediatric urology. Urol Clin North Am 51(1):91–103
Nedbal C et al (2023) The role of “artificial intelligence, machine learning, virtual reality, and radiomics” in PCNL: a review of publication trends over the last 30 years. Ther Adv Urol 15:17562872231196676
Article PubMed PubMed Central Google Scholar
Li J et al (2023) An artificial intelligence method for predicting postoperative urinary incontinence based on multiple anatomic parameters of MRI. Heliyon 9(10):e20337
Article PubMed PubMed Central Google Scholar
Bianchi G et al (2023) Artificial intelligence evaluation of confocal microscope prostate images: our preliminary experience. Minerva Urol Nephrol 75(5):545–547
Checcucci E et al (2020) Applications of neural networks in urology: a systematic review. Curr Opin Urol 30(6):788–807
Liu Y et al (2023) Heat shock protein family A member 8 is a prognostic marker for bladder cancer: evidences based on experiments and machine learning. J Cell Mol Med 27:3995
Article CAS PubMed PubMed Central Google Scholar
Flerlage T et al (2023) Mortality risk factors in pediatric onco-critical care patients and machine learning derived early onco-critical care phenotypes in a retrospective cohort. Crit Care Explor 5(10):e0976
Article PubMed PubMed Central Google Scholar
Wu Y et al (2023) A retrospective study using machine learning to develop predictive model to identify urinary infection stones in vivo. Urolithiasis 51(1):84
Article CAS PubMed PubMed Central Google Scholar
Haifler M et al (2022) A machine learning model for predicting surgical intervention in renal colic due to ureteral stone(s) < 5 mm. Sci Rep 12(1):11788
Article CAS PubMed PubMed Central Google Scholar
Katz JE et al (2023) The development of an artificial intelligence model based solely on computer tomography successfully predicts which patients will pass obstructing ureteral calculi. Urology 174:58–63
Dal Moro F et al (2006) A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines. Kidney Int 69(1):157–160
Kothari D, Patel M, Sharma AK (2021) Implementation of grey scale normalization in machine learning & artificial intelligence for bioinformatics using convolutional neural networks. In: 2021 6th international conference on inventive computation technologies (ICICT)
Ogutu JO, Piepho H-P, Schulz-Streeck T (2011) A comparison of random forests, boosting and support vector machines for genomic selection. BMC Proc 5(3):S11
Article PubMed PubMed Central Google Scholar
Yonazu S et al (2024) Cost-effectiveness analysis of the artificial intelligence diagnosis support system for early gastric cancers. DEN Open 4(1):e289
Manolakos D et al (2024) Use of an elastic-scattering spectroscopy and artificial intelligence device in the assessment of lesions suggestive of skin cancer: a comparative effectiveness study. JAAD Int 14:52–58
Pandey A et al (2023) A prospective evaluation of patient-reported outcomes during follow-up of ureteral stones managed with medical expulsive treatment (MET). Urolithiasis 51(1):56
Golomb D et al (2023) Spontaneous stone expulsion in patients with history of urolithiasis. Urologia 90(2):329–334
Aghaways I et al (2022) The role of inflammatory serum markers and ureteral wall thickness on spontaneous passage of ureteral stone < 10 mm: a prospective cohort study. Ann Med Surg (Lond) 80:104198
Sharma G et al (2022) Comparison of efficacy of three commonly used alpha-blockers as medical expulsive therapy for distal ureter stones: a systematic review and network meta-analysis. Int Braz J Urol 48(5):742–759
Imperatore V et al (2014) Medical expulsive therapy for distal ureteric stones: tamsulosin versus silodosin. Arch Ital Urol Androl 86(2):103–107
Comments (0)