<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>Sniffing breath to diagnose a disease has been practiced by doctors since ancient times. Nowadays, electronic noses are successfully used in the food, textile and perfume industry as well as for air pollution control. The aim of this study was to test whether exhaled breath analysed by an artificial nose could identify and discriminate between different lung diseases. A total of 76 individuals were tested: 25 bronchial asthma, 19 lung cancer, 10 pneumonia, 6 chronic obstructive pulmonary disease (COPD) patients and 16 healthy volunteers. Exhaled air was collected in plastic bags and immediately analysed using an electronic nose instrument (9185, Nordic Sensors AB) containing 14 different odour sensors. Multifactor logistic regression analysis was used to determine correlation between the amplitudes of sensor responses and the clinical diagnoses of patients and to calculate sensitivity and specificity of the method for each diagnosis. For diagnostics of asthma the sensitivity was found to be 84% and specificity — 86%. For lung cancer, the sensitivity was 74% and specificity, 95%; for pneumonia 90% and 98%, but for COPD, 33% and 97%, respectively. We conclude that an artificial nose is able to discriminate among different lung diseases with sufficiently good accuracy. This method may be further developed to implement it in clinical medicine for express diagnostics of acute and chronic lung diseases.</p></abstract>

Sniffing breath to diagnose a disease has been practiced by doctors since ancient times. Nowadays, electronic noses are successfully used in the food, textile and perfume industry as well as for air pollution control. The aim of this study was to test whether exhaled breath analysed by an artificial nose could identify and discriminate between different lung diseases. A total of 76 individuals were tested: 25 bronchial asthma, 19 lung cancer, 10 pneumonia, 6 chronic obstructive pulmonary disease (COPD) patients and 16 healthy volunteers. Exhaled air was collected in plastic bags and immediately analysed using an electronic nose instrument (9185, Nordic Sensors AB) containing 14 different odour sensors. Multifactor logistic regression analysis was used to determine correlation between the amplitudes of sensor responses and the clinical diagnoses of patients and to calculate sensitivity and specificity of the method for each diagnosis. For diagnostics of asthma the sensitivity was found to be 84% and specificity — 86%. For lung cancer, the sensitivity was 74% and specificity, 95%; for pneumonia 90% and 98%, but for COPD, 33% and 97%, respectively. We conclude that an artificial nose is able to discriminate among different lung diseases with sufficiently good accuracy. This method may be further developed to implement it in clinical medicine for express diagnostics of acute and chronic lung diseases.

Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors

Faculty of Medicine, University of Latvia, Šarlotes iela 1a, Rga, LV-1001, LATVIA

Pauls Stradiņš Clinical University Hospital, Pilsoū iela 13, Rga, LV-1002, LATVIA

Institute of Solid State Physics, University of Latvia, ēngaraga iela 8, Rga, LV-1021, LATVIA

Proceedings of the Latvian Academy of Sciences. Section B. Natural, Exact, and Applied Sciences.

Sniffing breath to diagnose a disease has been practiced by doctors since ancient times. Nowadays, electronic noses are successfully used in the food, textile...

Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors

Publié en ligne: 06 nov. 2009

Pages: 210 - 213

DOI: https://doi.org/10.2478/v10046-009-0043-4

Mots clés
exhaled air analysis, electronic nose, lung diseases

This content is open access.

Exhaled Air Analysis in Patients with Different Lung Diseases Using Artificial Odour Sensors

Publié en ligne: 06 nov. 2009

Pages: 210 - 213

DOI: https://doi.org/10.2478/v10046-009-0043-4

Mots clésexhaled air analysis, electronic nose, lung diseases

This content is open access.

Mots clés
exhaled air analysis, electronic nose, lung diseases