Evidence map›Paper›PMID 34535144›Full record

ReviewRespiratory research2021

The smell of lung disease: a review of the current status of electronic nose technology.

I G van der Sar, N Wijbenga, G Nakshbandi, J G J V Aerts, O C Manintveld, M S Wijsenbeek, M E Hellemons, C C Moor

Abstract readReview
In one paragraph

Review in Respiratory research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 6 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  11. The evolution of scientific knowledge in childhood asthma over time.European respiratory review : an official journal of the European Respiratory Society · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

I G van der Sar *Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
N Wijbenga *Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
G NakshbandiDepartment of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
J G J V AertsDepartment of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
O C ManintveldDepartment of Cardiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
M S WijsenbeekDepartment of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
M E Hellemons *Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
C C Moor *Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands. c.moor@erasmusmc.nl.ORCID http://orcid.org/0000-0002-5295-2877

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-time non-invasive diagnostic tool, and for monitoring disease course and therapeutic effects. To date, multiple eNoses have been developed and evaluated in clinical studies across a wide spectrum of lung diseases, mainly for diagnostic purposes. Heterogeneity in study design, analysis techniques, and differences between eNose devices currently hamper generalization and comparison of study results. Moreover, many pilot studies have been performed, while validation and implementation studies are scarce. These studies are needed before implementation in clinical practice can be realised. This review summarises the technical aspects of available eNose devices and the available evidence for clinical application of eNose technology in different lung diseases. Furthermore, recommendations for future research to pave the way for clinical implementation of eNose technology are provided.

Indexed as

Electronic NoseExhalationHumansLung DiseasesMachine LearningPrecision MedicineSmellBreath analysisElectronic noseMachine learningPersonalised medicineRespiratory medicineSensor technology

Identifiers

PMID34535144
PMCPMC8448171

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.