PURPOSE: Computerized artificial neural networks are analogous to biological neuronal systems. Since they may be trained to recognize the relevance of complex patterns in data, neural networks may be useful for decision making in the multifactorial management of ureteropelvic junction obstruction. We determine the ability of a customized neural network to predict sonographic outcome after pyeloplasty in children with ureteropelvic junction obstruction. MATERIALS AND METHODS: A data set was constructed with 242 demographic, clinical, radiological and surgical elements. We analyzed the available retrospective data in 100 consecutive children who underwent unilateral pyeloplasty for ureteropelvic junction obstruction chosen from all 144 surgically treated for ureteropelvic junction obstruction between 1993 and 1995. One radiologist reviewed all film data and provided a final sonographic outcome designation in each case. We wrote a set of computer programs to construct a neural network. A composite 4-layer network was built with output nodes representing 4 possible sonographic outcomes. The 100 patient data set was randomly divided into 84 training and 16 testing examples. RESULTS: The neural network correctly predicted all 5 of 5 significantly improved, 7 of 7 improved, 2 of 2 same and 2 of 2 worse sonogram results after pyeloplasty. Therefore, sensitivity and specificity were 100% for all 4 outcomes. Linear regression analysis of the data yielded inferior sensitivity and specificity values (52 to 94%), confirming that ureteropelvic junction obstruction is a nonlinear data analysis problem. CONCLUSIONS: The 100% accuracy, sensitivity and specificity of our neural network in this pilot study provide evidence of the value of the neural computational approach for the modern exploration and modeling of the clinical problem of pediatric ureteropelvic junction obstruction.
PURPOSE: Computerized artificial neural networks are analogous to biological neuronal systems. Since they may be trained to recognize the relevance of complex patterns in data, neural networks may be useful for decision making in the multifactorial management of ureteropelvic junction obstruction. We determine the ability of a customized neural network to predict sonographic outcome after pyeloplasty in children with ureteropelvic junction obstruction. MATERIALS AND METHODS: A data set was constructed with 242 demographic, clinical, radiological and surgical elements. We analyzed the available retrospective data in 100 consecutive children who underwent unilateral pyeloplasty for ureteropelvic junction obstruction chosen from all 144 surgically treated for ureteropelvic junction obstruction between 1993 and 1995. One radiologist reviewed all film data and provided a final sonographic outcome designation in each case. We wrote a set of computer programs to construct a neural network. A composite 4-layer network was built with output nodes representing 4 possible sonographic outcomes. The 100 patient data set was randomly divided into 84 training and 16 testing examples. RESULTS: The neural network correctly predicted all 5 of 5 significantly improved, 7 of 7 improved, 2 of 2 same and 2 of 2 worse sonogram results after pyeloplasty. Therefore, sensitivity and specificity were 100% for all 4 outcomes. Linear regression analysis of the data yielded inferior sensitivity and specificity values (52 to 94%), confirming that ureteropelvic junction obstruction is a nonlinear data analysis problem. CONCLUSIONS: The 100% accuracy, sensitivity and specificity of our neural network in this pilot study provide evidence of the value of the neural computational approach for the modern exploration and modeling of the clinical problem of pediatric ureteropelvic junction obstruction.