Electronic Nose Technology in Respiratory Diseases

Our plain-English summary of Electronic Nose Technology in Respiratory Diseases, by S. Dragonieri, G. Pennazza, P. Carratu, et al (Lung, 2017).

The study

• Authors: S. Dragonieri, G. Pennazza, P. Carratu, O. Resta
• Journal: Lung
• Published: 2017;195(2):157-165
• Type of article: Review
• Cited by: 88 articles, as counted by NIH iCite
• Read it: PubMed · Publisher

Abstract

Electronic noses (e-noses) are based on arrays of different sensor types that respond to specific features of an odorant molecule, mostly volatile organic compounds (VOCs). Differently from gas chromatography and mass spectrometry, e-noses can distinguish VOCs spectrum by pattern recognition. E-nose technology has successfully been used in commercial applications, including military, environmental, and food industry. Human-exhaled breath contains a mixture of over 3000 VOCs, which offers the postulate that e-nose technology can have medical applications. Based on the above hypothesis, an increasing number of studies have shown that breath profiling by e-nose could play a role in the diagnosis and/or screening of various respiratory and systemic diseases. The aim of the present study was to review the principal literature on the application of e-nose technology in respiratory diseases.

In plain English

This article was a review, not a new study. The authors reviewed published research on electronic noses (e-noses) in respiratory diseases, including asthma, COPD, respiratory infections, lung cancer, malignant pleural mesothelioma and sleep apnoea. E-noses are devices with arrays of sensors that recognize overall patterns ('breathprints') of volatile organic compounds in exhaled breath. The authors' table listed two studies on malignant pleural mesothelioma. In the first, by Dragonieri et al., the e-nose distinguished mesothelioma patients from people professionally exposed to asbestos with 80.8% accuracy, 92.3% sensitivity and 85.7% specificity. In the second, by Chapman et al., which included 20 mesothelioma patients, 18 people with asbestos-related diseases and 42 healthy controls, the e-nose discriminated mesothelioma patients with 90% sensitivity and 88% specificity. The authors stated several limitations. Most of the reviewed studies were first steps toward validation, and breath collection methods were not yet standardized. E-noses do not identify which compounds are disease-related, and commercial sensors were not designed for human breath. The authors concluded that if these challenges were overcome, e-noses could become convenient devices for detecting respiratory diseases, ideally as screening tools.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Narrative review of published literature on e-nose breath analysis in respiratory diseases; no search method is described
• Who was studied: Published clinical studies of exhaled-breath profiling by e-nose. The authors' table lists 8 on asthma, 8 on COPD, 14 on respiratory infections, 12 on lung cancer, 2 on malignant pleural mesothelioma, 6 on obstructive sleep apnoea syndrome, 2 on cystic fibrosis and primary ciliary dyskinesia, and 1 each on pulmonary embolism, pulmonary arterial hypertension, pulmonary sarcoidosis and acute respiratory distress syndrome
• Exposure: The subject reviewed is e-nose analysis of exhaled breath, not an exposure. Asbestos appears only in the mesothelioma studies, as a comparison group of 'individuals professionally exposed to asbestos' and of 'subjects with asbestos-related diseases'. No fiber types, jobs or doses are given.
• Main results:
  • Reported from a cited study by Dragonieri et al. (reference 55): patients with malignant pleural mesothelioma were distinguished from asbestos-exposed individuals with 80.8% accuracy, 92.3% sensitivity and 85.7% specificity
  • Same cited study (reference 55): mesothelioma patients were distinguished from healthy controls with 84.6% accuracy, 92.3% sensitivity and 69.2% specificity
  • Reported from a cited study by Chapman et al. (reference 56) of 20 patients with malignant pleural mesothelioma, 18 subjects with asbestos-related diseases and 42 healthy controls: mesothelioma patients were discriminated with 90% sensitivity and 88% specificity
  • Reported from a cited study by Di Natale et al. (reference 44): breath of 35 lung cancer patients was distinguished from 18 healthy controls with 100% sensitivity and 94% specificity
  • Reported from a cited asthma study (reference 7): mild and severe asthmatics were discriminated from age-matched non-smoking healthy controls with accuracy of 100% and 90%, respectively
  • Reported from a cited COPD study by Sibila et al. (reference 23): COPD breathprints were differentiated from healthy controls with 83% accuracy and an AUC-ROC of 0.93
• The authors' conclusion: The authors concluded that if the methodological challenges are overcome, e-noses could become convenient devices for physicians to detect respiratory diseases, ideally as high-sensitivity screening tools to exclude disease
• Limitations the authors state:
  • Most reviewed studies are only first steps toward diagnostic validation; external and prospective validation is still needed
  • Sampling set-ups and breath collection methods are not yet optimized or standardized
  • E-noses do not identify which individual compounds are disease-related
  • Raw sensor data are incompatible between e-noses, even from the same brand
  • Commercially available e-noses were built for broad uses, and their sensors are not designed for human breath

Topics

The National Library of Medicine indexes this study under subjects our site covers: Lung cancer.

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• An electronic nose distinguishes exhaled breath of patients with Malignant Pleural Mesothelioma from controls — S. Dragonieri, M.P. van der Schee, et al. (2012)
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