Identifying chronic rhinosinusitis without nasal polyps by analyzing aspirated nasal air with an electronic nose based on differential mobility spectrometry

Virtanen, Jussi; Kontunen, Anton; Numminen, Jura; Oksala, Niku; Rautiainen, Markus; Roine, Antti; Kivekäs, Ilkka

doi:10.1080/00016489.2022.2093397

Cited by 2 publications

(1 citation statement)

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“…These studies found potential in using eNose technology for detecting sinusitis. 22 , 23 Because only a very small proportion of patients had chronic sinusitis, this is very unlikely to have affected current outcomes. Nonetheless, the results in this consecutive series of LTx patients demonstrate proof of concept and the clear potential for its application.…”

Section: Discussionmentioning

confidence: 99%

Detection of Bacterial Colonization in Lung Transplant Recipients Using an Electronic Nose

Wijbenga,

de Jong,

Hoek

et al. 2023

Transplantation Direct

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Background. Bacterial colonization (BC) of the lower airways is common in lung transplant recipients (LTRs) and increases the risk of chronic lung allograft dysfunction. Diagnosis often requires bronchoscopy. Exhaled breath analysis using electronic nose (eNose) technology may noninvasively detect BC in LTRs. Therefore, we aimed to assess the diagnostic accuracy of an eNose to detect BC in LTRs. Methods. We performed a cross-sectional analysis within a prospective, single-center cohort study assessing the diagnostic accuracy of detecting BC using eNose technology in LTRs. In the outpatient clinic, consecutive LTR eNose measurements were collected. We assessed and classified the eNose measurements for the presence of BC. Using supervised machine learning, the diagnostic accuracy of eNose for BC was assessed in a random training and validation set. Model performance was evaluated using receiver operating characteristic analysis. Results. In total, 161 LTRs were included with 80 exclusions because of various reasons. Of the remaining 81 patients, 16 (20%) were classified as BC and 65 (80%) as non-BC. eNose-based classification of patients with and without BC provided an area under the curve of 0.82 in the training set and 0.97 in the validation set. Conclusions. Exhaled breath analysis using eNose technology has the potential to noninvasively detect BC.

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Section: Discussionmentioning

confidence: 99%