Infrared Analysis of Urinary Calculi by a Single Reflection Accessory and a Neural Network Interpretation Algorithm

Abstract

Abstract Background: Preparation of KBr tablets, used for Fourier transform infrared (FT-IR) analysis of urinary calculus composition, is time-consuming and often hampered by pellet breakage. We developed a new FT-IR method for urinary calculus analysis. This method makes use of a Golden Gate Single Refection Diamond Attenuated Total Reflection sample holder, a computer library, and an artificial neural network (ANN) for spectral interpretation. Methods: The library was prepared from 25 pure components and 236 binary and ternary mixtures of the 8 most commonly occurring components. The ANN was trained and validated with 248 similar mixtures and tested with 92 patient samples, respectively. Results: The optimum ANN model yielded root mean square errors of 1.5% and 2.3% for the training and validation sets, respectively. Fourteen simple expert rules were added to correct systematic network inaccuracies. Results of 92 consecutive patient samples were compared with those of a FT-IR method with KBr tablets, based on an initial computerized library search followed by visual inspection. The bias was significantly different from zero for brushite (−0.8%) and the concomitantly occurring whewellite (−2.8%) and weddellite (3.8%), but not for ammonium hydrogen urate (−0.1%), carbonate apatite (0.5%), cystine (0.0%), struvite (0.4%), and uric acid (−0.1%). The 95% level of agreement of all results was 9%. Conclusions: The new Golden Gate method is superior because of its smaller sample size, user-friendliness, robustness, and speed. Expert knowledge for spectral interpretation is minimized by the combination of a library search and ANN prediction, but visual inspection remains necessary.