Prediction of upper urinary tract calculi using an artificial neural network.
OBJECTIVE
To evaluate the possibility of using an artificial neural network (ANN) in upper urinary tract calculi prediction.


METHODS
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.


CONCLUSIONS
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.
Tanthanuch
Monthira
M
Department of Surgery, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand. mmonthir@ratree.psu.ac.th
Tanthanuch
Sawit
S
D006801:Humans; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D014545:Urinary Calculi / Q000175:diagnosis*