Author/Authors :
xu, zi-hao affiliated jiangning hospital with nanjing medical university - department of urology, Nanjing, China , zhou, shuang affiliated jiangning hospital with nanjing medical university - department of urology, Nanjing, China , jia, chun-ping affiliated jiangning hospital with nanjing medical university - department of urology, Nanjing, China , lv, jian-lin affiliated jiangning hospital with nanjing medical university - department of urology, Nanjing, China
Abstract :
Purpose: The cumulative effect of measurable parameters on proximal ureteral stone clearance following extracorporeal shock wave lithotripsy (ESWL) was assessed via the application of an artificial neural network (ANN). Methods and patients: From January 2015 to January 2020, 1182 patients with upper ureteral stone underwent ESWL in the supine position. The corresponding significance of each variable inputted in this network was determined by means of Wilks’ generalized likelihood ratio test. If the connection weight of a given variable could be set to zero while maximizing the accuracy of the network classification, the variable was not considered as an important predictor of stone removal. Results: A total of 1174 cases (after excluding 8 cases) were randomly assigned into a training group (813 cases), testing group (270 cases), and keeping group (91 cases). We performed ANN analysis of the stone clearance rate in the training group, and it showed a predictive accuracy of 93.2% (482/517 cases). However, the predictive accuracy for the stone clearance rate in the training group was 75.3% (223 cases/296 cases). The order of importance of independent variables was stone length course (d) patient’s age stone width pH value. Conclusion: The ANN possesses a huge prediction potential for the invalidation of ESWL.
