ArticleLung2025
Exhaled Breath Analysis Using a Novel Electronic Nose for Different Respiratory Disease Entities.
Article in Lung, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Few-Shot Lung Cancer Classification via Electronic Nose Using Large Language Models: A Multicentre Prospective Study.Respirology (Carlton, Vic.) · 2026Article
- Electronic nose technology in bronchial asthma diagnosis and management.Multidisciplinary respiratory medicine · 2026Article
- Harnessing breath biomarkers for pneumonia diagnosis and prognosis.Expert review of respiratory medicine · 2026Review
- Machine learning-enabled diagnosis of viral respiratory infections from exhaled volatile organic compound analysis.BMC pulmonary medicine · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
purposeElectronic noses (eNose) and gas chromatography mass spectrometry (GC-MS) are two important breath analysis approaches for differentiating between respiratory diseases. We evaluated the performance of a novel electronic nose for different respiratory diseases, and exhaled breath samples from patients were analyzed by GC-MS. MATERIALS AND
methodsPatients with lung cancer, pneumonia, structural lung diseases, and healthy controls were recruited (May 2019-July 2022). Exhaled breath samples were collected for eNose and GC-MS analysis. Breathprint features from eNose were analyzed using support vector machine model and leave-one-out cross-validation was performed.
resultsA total of 263 participants (including 95 lung cancer, 59 pneumonia, 71 structural lung disease, and 38 healthy participants) were included. Three-dimensional linear discriminant analysis (LDA) showed a clear distribution of breathprints. The overall accuracy of eNose for four groups was 0.738 (194/263). The accuracy was 0.86 (61/71), 0.81 (77/95), 0.53 (31/59), and 0.66 (25/38) for structural lung disease, lung cancer, pneumonia, and control groups respectively. Pair-wise diagnostic performance comparison revealed excellent discriminant power (AUC: 1-0.813) among four groups. The best performance was between structural lung disease and healthy controls (AUC: 1), followed by lung cancer and structural lung disease (AUC: 0.958). Volatile organic compounds revealed a high individual occurrence rate of cyclohexanone and N,N-dimethylacetamide in pneumonic patients, ethyl acetate in structural lung disease, and 2,3,4-trimethylhexane in lung cancer patients.
conclusionsOur study showed that the novel eNose effectively distinguishes respiratory diseases and holds potential as a point-of-care diagnostic tool, with GC-MS identifying candidate VOC biomarkers.
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