SWL literature
SWL Literature

Xu ZH. et al., 2021: Prediction of Proximal Ureteral Stones Clearance after Shock Wave Lithotripsy Using an Artificial Neural Network

Xu ZH, Zhou S, Jia CP, Lv JL.
Department of Urology, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, Jiangsu 211100, China.
Department of Urology, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, Jiangsu 211100, China.
Department of Urology, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, Jiangsu 211100, China.
Department of Urology, The Affiliated Jiangning Hospital with Nanjing Medical University, Nanjing, Jiangsu 211100, China.

Abstract

Purpose: The cumulative effect of measurable parameters on proximal ureteral stone clearance followed by the shock wave lithotripsy was assessed via the application of an artificial neural network.

Methods and patients: From January 2015 to January 2020, 1182 patients with upper ureteral stone underwent extracorporeal shock wave lithotripsy (ESWL) with supine position. The corresponding significance of each variable inputted in this network was determined by means of Wilk's generalized likelihood ratio test. If the connection weight of a given variable can be set to zero while maximizing the accuracy of the network classification, the variable is not considered an important predictor of stone removal.

Results: A total of 1174 cases (excluding 8 cases) were randomly assigned into a training group (813 cases), testing group (270 cases), and keeping group (91 cases). We evaluated artificial neural network analysis to the stone clearance rate of the training group, with a predictive accuracy of 93.2% (482/517 cases). While the predictive accuracy of the stone clearance rate of 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 neural network possess a huge prediction potential for the invalidation of ESWL.
Urol J. 2021 Feb 24. doi: 10.22037/uj.v18i.6476. Online ahead of print. PMID: 33638143. FREE ARTICLE

0
 

Comments 1

Peter Alken on Monday, May 31 2021 08:30

Urology Journal is the official journal of the Urology and Nephrology Research Center (UNRC) and the Iranian Urological Association (IUA). Authors have to pay: “Open access publishing is not without costs. Urology Journal defrays these costs through publication charges because it does not have subscription charges for its research content. The Urology Journal believes that immediate, worldwide, barrier-free, open access to the full text of research articles is in the best interests of the scientific community. The publication charge is 650 Euros for case reports, brief communication and Point of techniques. For Original Articles and review articles the publication charge is 750 Euros. Systematic reviews or meta-analyses invited by journal editors will not be subject to publication charge.”
The few papers dealing with artificial neural networks to predict ESWL success or failure (1-5) did not make it into relevant clinical use by others. I do not think that this one will perform better. Feeding well-known clinical data in a computer program will select some of these clinical data as relevant. The user should at least try to speculate why these data are important or if they only have a statistical but not a causal relationship with the message.
What causal effects have patient age or urinary pH on ESWL of ureteral stones? I don’t know.
The average age of the paper’s references on ESWL of urinary tract stones is 20 years!
1 Gomha MA, et al. Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model? J Urol. 2004 Jul;172(1):175-9. doi:10.1097/01.ju.0000128646.20349.27.

2 Seckiner I, et al. A neural network - based algorithm for predicting stone - free status after ESWL therapy. Int Braz J Urol. 2017 Nov-Dec;43(6):1110-1114. doi: 10.1590/S1677-5538.IBJU.2016.0630.

3 Hamid A, et al. Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU Int. 2003 Jun;91(9):821-4. doi: 10.1046/j.1464-410x.2003.04230.x.

4 Goyal NK, et al. A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy. Saudi J Kidney Dis Transpl. 2010 Nov;21(6):1073-80. PMID: 21060176.

5 Rice P, et al. Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis. Urology. 2021 Apr 21:S0090-4295(21)00331-9. doi: 10.1016/j.urology.2021.04.006.

Peter Alken

Urology Journal is the official journal of the Urology and Nephrology Research Center (UNRC) and the Iranian Urological Association (IUA). Authors have to pay: “Open access publishing is not without costs. Urology Journal defrays these costs through publication charges because it does not have subscription charges for its research content. The Urology Journal believes that immediate, worldwide, barrier-free, open access to the full text of research articles is in the best interests of the scientific community. The publication charge is 650 Euros for case reports, brief communication and Point of techniques. For Original Articles and review articles the publication charge is 750 Euros. Systematic reviews or meta-analyses invited by journal editors will not be subject to publication charge.” The few papers dealing with artificial neural networks to predict ESWL success or failure (1-5) did not make it into relevant clinical use by others. I do not think that this one will perform better. Feeding well-known clinical data in a computer program will select some of these clinical data as relevant. The user should at least try to speculate why these data are important or if they only have a statistical but not a causal relationship with the message. What causal effects have patient age or urinary pH on ESWL of ureteral stones? I don’t know. The average age of the paper’s references on ESWL of urinary tract stones is 20 years! 1 Gomha MA, et al. Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model? J Urol. 2004 Jul;172(1):175-9. doi:10.1097/01.ju.0000128646.20349.27. 2 Seckiner I, et al. A neural network - based algorithm for predicting stone - free status after ESWL therapy. Int Braz J Urol. 2017 Nov-Dec;43(6):1110-1114. doi: 10.1590/S1677-5538.IBJU.2016.0630. 3 Hamid A, et al. Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study. BJU Int. 2003 Jun;91(9):821-4. doi: 10.1046/j.1464-410x.2003.04230.x. 4 Goyal NK, et al. A comparative study of artificial neural network and multivariate regression analysis to analyze optimum renal stone fragmentation by extracorporeal shock wave lithotripsy. Saudi J Kidney Dis Transpl. 2010 Nov;21(6):1073-80. PMID: 21060176. 5 Rice P, et al. Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis. Urology. 2021 Apr 21:S0090-4295(21)00331-9. doi: 10.1016/j.urology.2021.04.006. Peter Alken
Guest
Tuesday, July 27 2021

By accepting you will be accessing a service provided by a third-party external to https://storzmedical.com/

Linkedin Channel Facebook Channel Instagram Channel Twitter Youtube Channel