Electronic nose: clinical diagnosis based on soft computing methodologies

Abstract

Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage and among others, urine volatile compounds have been identified as possible diagnostic markers. A newly developed electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with urinary tract infections, gastrointestinal and respiratory infections, and 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, and a classifier system based a neural networks, genetic algorithms, and multivariate techniques such as principle components analysis and discriminant function analysis-cross validation. The experimental results confirm the validility of the presented methods.