Related Experiment Video
Updated: Jun 11, 2025
Vessel-sparing Excision and Primary Anastomosis
Published on: January 7, 2019
Utilizing artificial neural network system to predict the residual valve after endoscopic posterior urethral valve
Mehdi Shirazi1, Zahra Jahanabadi2, Faisal Ahmed3
1Department of Urology, School of Medicine; Histomorphometry and Stereology Research Center, Shiraz University of Medical Sciences, Shiraz. shirazim@sums.ac.ir.
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.
