Artificial neural networks in predicting optimum XXXX by extracorporeal shock wave lithotripsy: a preliminary study.

@entity1 @entity1290
OBJECTIVE: To assess the ability of artificial neural networks (ANNs) to predict optimum @entity1290 in @entity1 being managed by extracorporeal shock wave lithotripsy (ESWL). @entity1 AND METHODS: The study included 82 @entity1 with @entity1290 who were being treated by ESWL. Data (input and output values) from 60 @entity1 in whom there was optimum fragmentation of stones by ESWL were used for training the ANN. These data mainly included the 24-h urinary variables, the radiological features of the stone disease and the ESWL settings used. The predictability of the trained ANN was tested on 22 subsequent @entity1 , by supplying the input variables of the 22 @entity1 into the trained ANN and recording the output values (predicted values). After subjecting these @entity1 to ESWL, the actual results (observed values) were recorded. The predicted and the observed values were then compared. RESULTS: In the 22 @entity1 in whom predictability was tested, the trained ANN predicted optimum fragmentation at < or = 13 000 shocks/stone (as per study protocol) in 17 and optimum fragmentation at> 13 000 shocks/stone in the other five. In the 17 @entity1 (test set) where the trained ANN had predicted optimum fragmentation at < or = 13 000 shocks/stone, the optimum fragmentation was at that value, although the predicted and observed values were not identical. The overall correlation between the predicted and the observed values was 75.5% (correlation coefficient 0.7547) in these 17 @entity1 . Of the other five @entity1 , none had optimum fragmentation at < 13 000 shocks/stone, as predicted by the trained ANN, giving complete accuracy for this factor. CONCLUSION: This was a pilot study, i.e. an initial attempt to use an ANN in this regard, and although there were few @entity1 , such that it is not possible to make final recommendations, the overall predictability was approximately 75%. An encouraging outcome of the study was that the trained ANN identified @entity1 unlikely to benefit from ESWL. Using a larger dataset and identifying more significant variables, while eliminating inputs with a negative effect, the efficiency and utility of this ANN can probably be enhanced and in future it might be possible to predict stone fragmentation with reasonable accuracy.
