Prediction of upper urinary tract calculi using an artificial neural network.

OBJECTIVES: To evaluate the possibility of using an artificial neural network (ANN) in upper urinary tract calculi prediction. MATERIAL AND METHOD: Data of 168 upper urinary tract calculi patients treated in the Division of Urology, Department of Surgery, Songklanagarind Hospital from January 1997 to December 2000 were reviewed and classified into 6 catagories and 20 characteristics. 100 items were used in training and 68 in testing for an ANN designed with 3 layers: 20 nodes for an input layer, 5 nodes for a hidden layer and a node for the output. RESULTS: Output data between 0-0.38 indicate free of calculi, 0.65-1 indicate prone to have calculi, 0.38-0.65 indicate probable calculi and further need investigation. CONCLUSION: An ANN with error back-propagation training can be used in diagnosing the presence of upper urinary tract calculi. The accuracy of prediction depends on a previous history of calculi, nephrocalcinosis, 24 hour urine assay for citrate and urine culture.