Identification of urinary track infections using soft computing techniques

Abstract

Sensorial analysis based on the utilisation of human senses, is one of the most important investigation methods in food and chemical analysis. Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. An array of gas sensors has been employed to identify in vivo urine samples from patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK Health Laboratory environment. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on intelligent techniques has been developed. The implementation of an advanced neural network scheme and the concept of fusion of multiple classifiers dedicated to specific feature parameters have been adopted in this study. The experimental results confirm the validity of the presented methods. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology.