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使用人工神经网络预测冲击波碎石术后下极结石清除情况。

Prediction of lower pole stone clearance after shock wave lithotripsy using an artificial neural network.

作者信息

Poulakis Vassilis, Dahm Philipp, Witzsch Ulrich, de Vries Rachelle, Remplik Jochem, Becht Eduard

机构信息

Department of Urology and Pediatric Urology, Krankenhaus Nordwest, Teaching Hospital of Johann-Wolfgang-Goethe-University, Frankfurt, Frankfurt/Main, Germany.

出版信息

J Urol. 2003 Apr;169(4):1250-6. doi: 10.1097/01.ju.0000055624.65386.b9.

Abstract

PURPOSE

We performed this study as a comprehensive evaluation of variables reported to affect lower pole stone clearance after shock wave lithotripsy using artificial neural network analysis.

MATERIALS AND METHODS

The radiographic images and treatment records of 680 patients with lower pole renal calculi treated with primary shock wave lithotripsy using the Wolf Piezolith 2500 (Wolf, Knittlingen, Germany) lithotriptor were retrospectively evaluated by applying artificial neural network analysis. Successful stone clearance was defined as absent fragments of any size detected on plain x-ray with tomography and/or excretory pyelography performed 6 months after treatment. Prognostic variables included patient characteristics, laboratory values, stone characteristics and the spatial anatomy of the lower pole, as defined by infundibular length, diameter, caliceal pelvic height, 2 measurements of the lower infundibulopelvic and infundibuloureteropelvic angles as well as the pattern of dynamic urinary transport.

RESULTS

Artificial neural network analysis had 92% accuracy for correctly predicting lower pole stone clearance. The pattern of dynamic urinary transport represented the most influential predictor of stone clearance, followed by a measure of the infundibuloureteropelvic angle, body mass index, caliceal pelvic height and stone size. Anatomical measurements of lower pole anatomy and classification of the type of urinary transport were well reproducible with low intra-observer and interobserver variability (correlation coefficient alpha >0.8).

CONCLUSIONS

In a comprehensive analysis of variables reported to influence lower pole stone clearance artificial neural network analysis predicted stone clearance with a high degree of accuracy. The relative importance of dynamic urinary transport in lower pole stones and the usefulness of artificial neural network analysis to predict shock wave lithotripsy outcomes in individuals must be confirmed in a prospective trial.

摘要

目的

我们开展本研究,旨在通过人工神经网络分析,对据报道会影响冲击波碎石术后下极结石清除率的变量进行全面评估。

材料与方法

回顾性评估680例采用Wolf Piezolith 2500(德国克尼廷根市Wolf公司)碎石机进行初次冲击波碎石治疗的下极肾结石患者的影像学图像和治疗记录,并应用人工神经网络分析。成功的结石清除定义为治疗6个月后,在X线平片联合断层扫描和/或排泄性肾盂造影检查中未检测到任何大小的结石碎片。预后变量包括患者特征、实验室检查值、结石特征以及下极的空间解剖结构,下极的空间解剖结构由漏斗部长度、直径、肾盂肾盏高度、下漏斗肾盂角和漏斗输尿管肾盂角的2项测量值以及动态尿液输送模式定义。

结果

人工神经网络分析对正确预测下极结石清除率的准确率为92%。动态尿液输送模式是结石清除最具影响力的预测指标,其次是漏斗输尿管肾盂角测量值、体重指数、肾盂肾盏高度和结石大小。下极解剖结构的测量以及尿液输送类型的分类具有良好的可重复性,观察者内和观察者间的变异性较低(相关系数α>0.8)。

结论

在对据报道会影响下极结石清除率的变量进行全面分析时,人工神经网络分析能高度准确地预测结石清除情况。动态尿液输送在下极结石中的相对重要性以及人工神经网络分析对预测个体冲击波碎石治疗结果的有用性,必须在前瞻性试验中得到证实。

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