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Sniffing out the truth: Clinical diagnosis using the electronic nose
Cranfield University, Postgrad Med Sch, Bedford MK43 0AL, England; Cranfield University, Institute Biosci and Technology, Silsoe, Beds, England; .
Cranfield University, UK.ORCID iD: 0000-0002-1815-9699
2000 (English)In: Clinical Chemistry and Laboratory Medicine, ISSN 1434-6621, Vol. 38, no 2, 99-112 p.Article in journal (Refereed) Published
Abstract [en]

Recently the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence (AI). It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage. Later chromatographic techniques identified an enormous number of volatiles in human clinical specimens that might serve as potential disease markers. "Artificial nose" technology has been employed in several areas of medical diagnosis, including rapid detection of tuberculosis (TB), Helicobacter pylori (HP) and urinary tract infections (UTI). Preliminary results have demonstrated the possibility of identifying and characterising microbial pathogens in clinical specimens. A hybrid intelligent model of four interdependent "tools", odour generation "kits", rapid volatile delivery and recovery systems, consistent low drift sensor performance and a hybrid intelligent system of parallel neural networks (NN) and expert systems, have been applied in gastric, pulmonary and urine diagnosis. Initial clinical tests have shown that it may be possible in the near future to use electronic nose technology not only for the rapid detection of diseases such as peptic ulceration, UTI, and TB but also for the continuous dynamic monitoring of disease stages. Major advances in information and gas sensor technology could enhance the diagnostic power of future bio-electronic noses and facilitate global surveillance models of disease control and management.

Place, publisher, year, edition, pages
Walter de Gruyter , 2000. Vol. 38, no 2, 99-112 p.
Keyword [en]
olfactory diagnosis; gas sensors; neural networks; genetic algorithms; multivariate analysis; infectious diseases
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-65237ISI: 000087683000005OAI: diva2:394997
Available from: 2011-02-04 Created: 2011-02-04 Last updated: 2013-10-04

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