Use of artificial neural networks in the management of antenatally diagnosed ureteropelvic junction obstruction

Background:

In this study, an artificial neural network (ANN) based system has been developed specifically to help in the management of antenatally diagnosed uretero-pelvic junction (UPJ) obstruction.

Methods:

A total of 53 infants with antenatally detected hydronephrosis caused by UPJ obstruction were included in this study. A neural network was developed with the help of a commercially available software package. The patients’ age and sex, renal pelvic diameter, laterality, split renal function and presence of renal scar on radionuclide scan, follow-up times, urine culture results and the presence of symptomatic infections were used as variables. These data were also entered into a statistical software package and linear regression analysis was done.

Results:

During the follow-up period, 36 children were observed, and the remaining 17 renal units underwent pyeloplasty. The average sensitivity of the ANN model in predicting the outcome was found to be 92% in the training group and 75% in the validation and test groups. In linear regression, none of the predictors were found to be statistically significant.

Interpretation:

In this study, we have demonstrated that the use of ANNs in antenatally diagnosed UPJ obstruction can help the clinician in making treatment decisions, and thus can be useful in daily clinical practice.