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Letter to the editor for the article "A machine learning approach using stone volume to predict stone-free status at ureteroscopy".

作者信息

Altıntaş Emre, Şahin Ali, Gül Murat

机构信息

Department of Urology, Selcuk University School of Medicine, Konya, 42250, Turkey.

Selcuk University School of Medicine, Konya, 42250, Turkey.

出版信息

World J Urol. 2024 Aug 22;42(1):492. doi: 10.1007/s00345-024-05198-5.

DOI:10.1007/s00345-024-05198-5
PMID:39172157
Abstract
摘要

相似文献

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Letter to the editor for the article "A machine learning approach using stone volume to predict stone-free status at ureteroscopy".致编辑的信:关于《一种使用结石体积预测输尿管镜检查无结石状态的机器学习方法》一文
World J Urol. 2024 Aug 22;42(1):492. doi: 10.1007/s00345-024-05198-5.
2
Ureteroscopy for the management of stone disease.输尿管镜术在结石病治疗中的应用。
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Which is the best way to treat a stone on a flexible ureterorrenoscopy? | Opinion: Dusting.在软性输尿管肾镜下治疗结石的最佳方法是什么?|观点:粉末化。
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Evaluation of preoperative measurement of stone surface area as a predictor of stone-free status after combined ureteroscopy with holmium laser lithotripsy: a single-center experience.评价术前结石表面积测量对钬激光联合输尿管镜碎石取石术后无结石状态的预测价值:单中心经验。
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A machine learning approach using stone volume to predict stone-free status at ureteroscopy.一种使用结石体积的机器学习方法来预测输尿管镜检查后的无结石状态。
World J Urol. 2024 May 22;42(1):344. doi: 10.1007/s00345-024-05054-6.
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Impact of surgical experience on stone-free rates of ureteroscopy for single urinary calculi of the upper urinary tract: a matched-paired analysis of 600 patients.手术经验对上尿路单发尿石症输尿管镜取石率的影响:600例患者的配对分析
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Eur Urol Focus. 2023 Sep;9(5):773-780. doi: 10.1016/j.euf.2023.03.010. Epub 2023 Apr 6.
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Cochrane Database Syst Rev. 2019 Feb 6;2(2):CD012703. doi: 10.1002/14651858.CD012703.pub2.

本文引用的文献

1
A machine learning approach using stone volume to predict stone-free status at ureteroscopy.一种使用结石体积的机器学习方法来预测输尿管镜检查后的无结石状态。
World J Urol. 2024 May 22;42(1):344. doi: 10.1007/s00345-024-05054-6.
2
Machine learning algorithm predicts urethral stricture following transurethral prostate resection.机器学习算法预测经尿道前列腺切除术后尿道狭窄。
World J Urol. 2024 May 15;42(1):324. doi: 10.1007/s00345-024-05017-x.
3
Faecal microbiome-based machine learning for multi-class disease diagnosis.基于粪便微生物组的机器学习用于多类疾病诊断。
Nat Commun. 2022 Nov 10;13(1):6818. doi: 10.1038/s41467-022-34405-3.
4
Tree-based Machine Learning Methods for Survey Research.用于调查研究的基于树的机器学习方法。
Surv Res Methods. 2019 Apr 11;13(1):73-93.