Title: | Artificial odor discrimination system using electronic nose and neural networks for the identification of urinary tract infection |
Author(s): | Kodogiannis VS; Lygouras JN; Tarczynski A; Chowdrey HS; |
Address: | "Centre for Systems Analysis, School of Computer Science, University of Westminster, London HA1 3TP, UK. kodogiv@wmin.ac.uk" |
Journal Title: | IEEE Trans Inf Technol Biomed |
ISSN/ISBN: | 1558-0032 (Electronic) 1089-7771 (Linking) |
Abstract: | "Current clinical diagnostics are based on biochemical, immunological, or microbiological methods. However, these methods are operator dependent, time-consuming, expensive, and require special skills, and are therefore, not suitable for point-of-care testing. Recent developments in gas-sensing technology and pattern recognition methods make electronic nose technology an interesting alternative for medical point-of-care devices. An electronic nose has been used to detect urinary tract infection from 45 suspected cases that were sent for analysis in a U.K. Public Health Registry. These samples were analyzed by incubation in a volatile generation test tube system for 4-5 h. Two issues are being addressed, including the implementation of an advanced neural network, based on a modified expectation maximization scheme that incorporates a dynamic structure methodology and the concept of a fusion of multiple classifiers dedicated to specific feature parameters. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology" |
Keywords: | "Algorithms Artificial Intelligence Diagnostic Techniques, Urological/*instrumentation Electronics, Medical Fuzzy Logic Humans *Neural Networks, Computer Odorants/*analysis Point-of-Care Systems Robotics/instrumentation/methods Smell Urinary Tract Infectio;" |
Notes: | "MedlineKodogiannis, Vassilis S Lygouras, John N Tarczynski, Andrzej Chowdrey, Hardial S eng 2008/11/13 IEEE Trans Inf Technol Biomed. 2008 Nov; 12(6):707-13. doi: 10.1109/TITB.2008.917928" |