Diagnosing Lung And Gastric Cancers Through Exhaled Breath Analysis By Using Electronic Nose Technology Combined With Pattern Recognition Methods

Bouchikhi, Benachir; Zaim, Omar; Bari, Nezha El; Lagdali, N; Benelbarhdadi, I; Ajana, F.Z.

doi:10.1109/sensors47087.2021.9639700

Cited by 5 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The obtained concentration factors, calculated for acetone, were up 16.35 at 30 min adsorption time. The main advantage of the LTCC micropreconcentrator is the possibility of integrating it with a matrix of gas sensors manufactured using this method [25,26].…”

Section: Discussionmentioning

confidence: 99%

“…The course of the disease and its advancement, treatment methods and medications also affect the composition of the exhaled air [13]. Biomarkers are used to diagnose diseases, predict the risk of developing a disease or monitor the course of treatment and assess the effectiveness of therapies used in diseases such as asthma [14][15][16][17][18], cancer [13,[19][20][21][22][23][24][25][26], chronic obstructive pulmonary disease [13,24,27,28] and diabetes [6,[29][30][31][32][33][34][35][36][37]. Selected biomarkers of diseases in breath were shown in table 1.…”

Section: Myocardial Infarctionmentioning

confidence: 99%

See 1 more Smart Citation

Review of the algorithms used in exhaled breath analysis for the detection of diabetes

Paleczek

Rydosz

2022

J. Breath Res.

View full text Add to dashboard Cite

Currently, intensive work is underway on the development of truly noninvasive medical diagnostic systems, including respiratory analysers based on the detection of biomarkers of several diseases including diabetes. In terms of diabetes, acetone is considered as a one of the potential biomarker, although is not the single one. Therefore, the selective detection is crucial. Most often, the analysers of exhaled breath are based on the utilization of several commercially available gas sensors or on specially designed and manufactured gas sensors to obtain the highest selectivity and sensitivity to diabetes biomarkers present in the exhaled air. An important part of each system are the algorithms that are trained to detect diabetes based on data obtained from sensor matrices. The prepared review of the literature showed that there are many limitations in the development of the versatile breath analyser, such as high metabolic variability between patients, but the results obtained by researchers using the algorithms described in this paper are very promising and most of them achieve over 90% accuracy in the detection of diabetes in exhaled air. This paper summarizes the results using various measurement systems, feature extraction and feature selection methods as well as algorithms such as Support Vector Machines, k-Nearest Neighbours and various variations of Neural Networks for the detection of diabetes in patient samples and simulated artificial breath samples.

show abstract

Section: Discussionmentioning

confidence: 99%