The use of neural networks for predicting the result of endoscopic treatment for vesico-ureteric reflux.
OBJECTIVE
To create an artificial neural network (ANN) to aid in predicting the results of endoscopic treatment for vesico-ureteric reflux (VUR).


METHODS
During 1999-2001 we used endoscopic treatment in 261 ureteric units with VUR of all grades and causes. An ANN based on multilayer perceptron architecture was created using an 11 x 6 x 1 structure, taking the following as variables: the cause and grade of VUR, the patient's age and sex, the type of implanted substance and its volume, the number of treatments, the affected ureter, the endoscopic findings, and the type of cystography used. In all, 174 cases were used as training samples for the ANN and 87 to validate it. We calculated the sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and the success rate (%) of the system.


RESULTS
In the training group the ANN gave a sensitivity of 86.4%, a specificity of 89.5%, a PPV of 76% and NPV of 94%, with a success rate of 88.6%. In the same training group logistic regression (LR) gave respective values of 68.2%, 58.8%, 39%, 82.7% and 61.4%. In the validation group the respective values for the ANN were 71.4%, 81.6%, 58.8%, 88.6% and 78.9%, and in the same validation group the LR gave 64.4%, 50%, 32.1%, 79.2% and 53.9%. The Wilcoxon test confirmed the independence of both methods (P < 0.001).


CONCLUSIONS
The ANN is an effective tool for assisting the urologist in indicating and applying endoscopic treatments for VUR.
Serrano-Durbá
Agustín
A
Urology Service, La Fe Children's Hospital, Valencia, Spain. serrano_agu@gva.es
Serrano
Antonio J
AJ
Magdalena
José R
JR
Martín
José D
JD
Soria
Emilio
E
Domínguez
Carlos
C
Estornell
Francisco
F
García-Ibarra
Fernando
F
D000293:Adolescent; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D008297:Male; D016571:Neural Networks, Computer; D012372:ROC Curve; D012680:Sensitivity and Specificity; D018666:Ureteroscopy / Q000379:methods*; D014718:Vesico-Ureteral Reflux / Q000601:surgery*