https://doi.org/10.4081/aiua.2024.12530

Utilizing artificial neural network system to predict the residual valve after endoscopic posterior urethral valve ablation

Vol. 96 No. 3 (2024)
Submitted: April 1, 2024
Accepted: June 1, 2024
Published: October 2, 2024
Artificial neural network, artificial intelligence, children, outcome, posterior urethral valve, residual valve
Abstract Views: 384
PDF: 114
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Mehdi Shirazi
Department of Urology, School of Medicine; Histomorphometry and Stereology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran, Islamic Republic of.
Zahra Jahanabadi
z_jahanabadi@yahoo.com
Department of Urology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran, Islamic Republic of.
Faisal Ahmed
https://orcid.org/0000-0001-7188-2715
Department of Urology, School of Medicine, Ibb University, Ibb, Yemen.
Davood Goodarzi
Department of Urology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran, Islamic Republic of.
Alimohammad Keshtvarz Hesam Abadi
Department of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran, Islamic Republic of.
Mohammad Reza Askarpour
Department of Urology, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran, Islamic Republic of.
Sania Shirazi
https://orcid.org/0000-0001-9461-9709
Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran, Islamic Republic of.

Purpose: To build, train, and assess the artificial neural network (ANN) system in estimating the residual valve rate after endoscopic valve ablation and compare the data obtained with conventional analysis.
Methods: In a retrospective cross-sectional study between June 2010 and December 2020, 144 children with a history of posterior urethral valve (PUV) who underwent endoscopic valve ablation were enrolled in the study. MATLAB software was used to design and train the network in a feed-forward backpropagation error adjustment scheme. Preoperative and postoperative data from 101 patients (70%) (training set) were utilized to assess the impact and relative significance of the necessity for repeated ablation. The validated suitably trained ANN was used to predict repeated ablation in the next 33 patients (22.9%) (test set) whose preoperative data were serially input into the system. To assess system accuracy in forecasting the requirement for repeat ablation, projected values were compared to actual outcomes. The likelihood of predicting the residual valve was calculated using a three-layered backpropagating deep ANN using preoperative and postoperative information.
Results: Of 144 operated cases, 33 (22.9%) had residual valves and needs to repeated ablation. The ANN accuracy, sensitivity, and specificity for predicting the residual valve were 90.75%, 92.73%, and 73.19%, respectively. Younger age at surgery, hyperechogenicity of the renal parenchyma, presence of vesicoureteral reflux (VUR), and grade of reflux before surgery were among the most significant characteristics that affected postoperative outcome variables, the need for repeated ablation, and were given the highest relative weight by the ANN system. 
Conclusions: The ANN is an integrated data-gathering tool for analyzing and finding relationships among variables as a complex non-linear statistical model. The results indicate that ANN is a valuable tool for outcome prediction of the residual valve after endoscopic valve ablation in patients with PUV.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Crossref
Scopus
Google Scholar
Europe PMC
Krishnan A, de Souza A, Konijeti R, Baskin LS. The anatomy and embryology of posterior urethral valves. J Urol. 2006;175:1214-20. DOI: https://doi.org/10.1016/S0022-5347(05)00642-7
Buffin-Meyer B, Tkaczyk M, Stanczyk M, et al. A single-center study to evaluate the efficacy of a fetal urine peptide signature predicting postnatal renal outcome in fetuses with posterior urethral valves.Pediatr Nephrol. 2020;35:469-75. DOI: https://doi.org/10.1007/s00467-019-04390-9
Holmdahl G, Sillen U. Boys with posterior urethral valves: outcome concerning renal function, bladder function and paternity at ages 31 to 44 years. J Urol. 2005;174:1031-4; discussion 4. DOI: https://doi.org/10.1097/01.ju.0000170233.87210.4f
Shirazi M, Farsiani M, Natami M, et al. Which patients are at higher risk for residual valves after posterior urethral valve ablation? Korean J Urol. 2014;55:64-8. DOI: https://doi.org/10.4111/kju.2014.55.1.64
Mirone V, Imbimbo C, Longo N, Fusco F. The detrusor muscle: an innocent victim of bladder outlet obstruction. Eur Urol. 2007;51:57-66. DOI: https://doi.org/10.1016/j.eururo.2006.07.050
Bani Hani O, Prelog K, Smith GH. A method to assess posterior urethral valve ablation. J Urol. 2006;176:303-5. DOI: https://doi.org/10.1016/S0022-5347(06)00562-3
Smeulders N, Makin E, Desai D, et al. The predictive value of a repeat micturating cystourethrogram for remnant leaflets after primary endoscopic ablation of posterior urethral valves. J Pediatr Urol. 2011;7:203-8. DOI: https://doi.org/10.1016/j.jpurol.2010.04.011
Nawaz G, Hussain I, Muhammad S, et al. Justification for re-look cystoscopy after posterior urethral valve fulguration. J Ayub Med Coll Abbottabad. 2017;29:30-2.
Lorenzo AJ, Rickard M, Braga LH, et al. Predictive analytics and modeling employing machine learning technology: the next step in data sharing, analysis, and individualized counseling explored with a large, prospective prenatal hydronephrosis database. Urology. 2019;123:204-9. DOI: https://doi.org/10.1016/j.urology.2018.05.041
Rajan P, Tolley DA. Artificial neural networks in urolithiasis. Curr Opin Urol. 2005;15:133-7. DOI: https://doi.org/10.1097/01.mou.0000160629.81978.7a
Hameed BMZ, AVLS Dhavileswarapu, Raza SZ, et al. Artificial intelligence and its impact on urological diseases and management: a comprehensive review of the literature. J Clin Med. 2021;10:1864. DOI: https://doi.org/10.3390/jcm10091864
Aminsharifi A, Irani D, Pooyesh S, et al. Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J Endourol. 2017;31:461-7. DOI: https://doi.org/10.1089/end.2016.0791
Oktar T, Salabas E, Acar O, et al. Residual valve and stricture after posterior urethral valve ablation: how to evaluate? J Pediatr Urol. 2013;9:184-7. DOI: https://doi.org/10.1016/j.jpurol.2012.01.016
Wu CQ, Blum ES, Patil D, Smith EA. Posterior urethral morphology on initial voiding cystourethrogram correlates to early renal outcomes in infants with posterior urethral valves. J Pediatr Urol. 2022;18:813-9. DOI: https://doi.org/10.1016/j.jpurol.2022.06.002
Gaibie Z, Mahomed N, Petersen KL, et al. Can the posterior:anterior urethral ratio on voiding cystourethrogram be used as a reliable predictor of successful posterior urethral valve ablation in male children? SA J Radiol. 2020;24:1820. DOI: https://doi.org/10.4102/sajr.v24i1.1820
Lawrence J,. Introduction to neural networks : design, theory and applications. Nevada City, California Scientific Software; 1994.
Ertin E. Mathematical methods for neural network analysis and design. Cambridge, MIT Press; 1996.
Mo Z, Li M, Xie X, et al. Urodynamic changes before and after endoscopic valve ablation in boys diagnosed with the posterior urethral valve without chronic renal failure. BMC Urol. 2023;23:5. DOI: https://doi.org/10.1186/s12894-022-01170-w
Deshpande AV, Alsaywid BS, Smith GH. Setting the speed limit: a pilot study of the rate of serum creatinine decrease after endoscopic valve ablation in neonates. J Urol. 2011;185:2497-500. DOI: https://doi.org/10.1016/j.juro.2011.01.020
Klaus R, Lange-Sperandio B. Chronic Kidney disease in boys with posterior urethral valves-pathogenesis, prognosis and management. Biomedicines. 2022;10:1894. DOI: https://doi.org/10.3390/biomedicines10081894
Long CJ, Bowen DK. Predicting and modifying risk for development of renal failure in boys with posterior urethral valves. Curr Urol Rep. 2018;19:55. DOI: https://doi.org/10.1007/s11934-018-0801-4
Nabil A, Salem A, Salah M, et al. The importance of second look cystoscopy after posterior urethral valve ablation in children: single center experience. Clin Surg. 2019;4:2691.
Motiwala T, Sinha A, Rathod KJ, et al. Correlation of urethral ratio and bladder wall thickness with cystoscopic findings in posterior urethral valve patients to assess residual valves. J Indian Assoc Pediatr Surg. 2022;27:53-9. DOI: https://doi.org/10.4103/jiaps.JIAPS_318_20
Bilgutay AN, Roth DR, Gonzales ET, Jr., et al. Posterior urethral valves: Risk factors for progression to renal failure. J Pediatr Urol. 2016;12:179 e1-7. DOI: https://doi.org/10.1016/j.jpurol.2015.10.009
Hennus PM, van der Heijden GJ, Bosch JL, et al. A systematic review on renal and bladder dysfunction after endoscopic treatment of infravesical obstruction in boys. PLoS One. 2012;7:e44663. DOI: https://doi.org/10.1371/journal.pone.0044663
Popovics P, Penniston KL. Current research and future directions in non-malignant urologic research - proceedings of the annual CAIRIBU meeting. Am J Clin Exp Urol. 2022;10:449-61.
Checcucci E, Autorino R, Cacciamani GE, et al. Artificial intelligence and neural networks in urology: current clinical applications. Minerva Urol Nefrol. 2020;72:49-57. DOI: https://doi.org/10.23736/S0393-2249.19.03613-0
Anagnostou T, Remzi M, Lykourinas M, Djavan B. Artificial neural networks for decision-making in urologic oncology. Eur Urol. 2003;43:596-603. DOI: https://doi.org/10.1016/S0302-2838(03)00133-7
Weaver JK, Milford K, Rickard M, et al. Deep learning imaging features derived from kidney ultrasounds predict chronic kidney disease progression in children with posterior urethral valves. Pediatr Nephrol. 2023;38:839-46. DOI: https://doi.org/10.1007/s00467-022-05677-0
Abdovic S, Cuk M, Cekada N, et al. Predicting posterior urethral obstruction in boys with lower urinary tract symptoms using deep artificial neural network. World J Urol. 2019;37:1973-9. DOI: https://doi.org/10.1007/s00345-018-2588-9

How to Cite

Shirazi, M., Jahanabadi, Z., Ahmed, F., Goodarzi , D., Hesam Abadi, A. K., Askarpour, M. R., & Shirazi, S. (2024). Utilizing artificial neural network system to predict the residual valve after endoscopic posterior urethral valve ablation. Archivio Italiano Di Urologia E Andrologia, 96(3). https://doi.org/10.4081/aiua.2024.12530
Copyright (c) 2024 the Author(s) Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.