Prediction of Proximal Ureteral Stones Clearance after Shock Wave Lithotripsy Using an Artificial Neural Network Prediction of Proximal Ureteral Stones clearance
Vol. 18 No. 05 (2021),
7 November 2021
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.
- Prediction, Proximal Ureteral,Stones, artificial neural network
How to Cite
2. Madbouly, K., Sheir, K. Z. and Elsobky, E. Impact of lower pole renal anatomy on stone clearance after shock wave lithotripsy: fact or friction? J Urol. 165, 1415(2001).
3. Ackermann, D. K., et al. Prognosis after extracorporeal shock wave lithotripsy of radiopaque renal calculi: a multivariate analysis. Eur Urol. 25, 105(1994).
4. Snow, P. B., Rodvold, D. M. and Brandt, J. M. Artificial neural networks in clinical urology. Urology.54, 787(1999).
5.Robert,M.,et al. Piezoelectric extracorporeal shock-wave lithotripsy of lower pole nephrolithiasis. Eur Urol. 32, 301(1997).
6. Pace, K. T., et al. Mechanical percussion, inversion and diuresis for residual lower pole fragments after shock wave lithotripsy: a prospective, single blind, randomized controlled trial. J Urol. 166,2065(2001).
7. Lamb, D. J. and Niederberger, C. S. Artificial intelligence in medicine and male infertility. World J Urol. 11, 129(1993).
8. Moul, J. W., Snow, P. B., Fernandez, E. B., Maher, P. D. and Sesterhenn, I. A. Neural network analysis of quantitative histological factors to predict pathological stage in clinical stage I nonseminomatous testicular cancer. J Urol. 153, 1674(1995).
9. Zlotta, A. R., et al. An artificial neural network for prostate cancer staging when serum prostate specific antigen is 10 ng./ml. or less. J Urol.169,1724(2003).
10, Gomha M A, 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?. Journal of Urology. 172,175-9(2004).
11. Cummings, J. M., Boullier, J. A., Izenberg, S. D., Kitchens, D. M. and Kothandapani, R. V. Prediction of spontaneous ureteral calculous passage by an artificial neural network. J Urol. 164,326(2000)
12. Gomha, M. A., Sheir, K. Z., Shawky, S., Mokhtar, A. A., Madbouly, K. and Ghoneim, M. A. Neural network to predict stone-free status after ESWL of renal stones. J Urol, suppl. 1999, 161: 376, abstract 1457.
13. Michaels, E. K., Niederberger, C. S., Golden, R. M., Brown, B.,Cho, L. and Hong, Y. Use of a neural network to predict stone growth after shock wave lithotripsy. Urology, 1998, 51: 335.
- Abstract Viewed: 0 times
- 6476/pdf Downloaded: 0 times