Determination of the composition of human urinary calculi composed of whewellite, weddellite and carbonate apatite using artificial neural networks

More than half of the analyzed calculi from patients from Macedonia are composed of whewellite, weddellite and carbonate apatite (as single components or in binary or ternary mixtures). In order to develop a simple and satisfactorily reliable method for quantitative analysis of urinary calculi, the possibility was explored to employ artificial neural networks (ANNs) as a tool for such a purpose. By changing the number of input and hidden neurons, a search was made for the three-layered feed-forward ANN which would give the best performance. The root-mean-square errors (RMSE) for the test samples are: 0.035 for whewellite, 0.064 for weddellite and 0.078 for carbonate apatite. The accuracy of the method was checked using standard-addition method on real samples. The discrepancies between calculated and predicted mass fraction of constituents were in the range acceptable for use of the proposed method in clinical laboratories.