Sensitivity and specificity of an electronic nose in diagnosing pulmonary tuberculosis among patients with suspected tuberculosis

Saktiawati, Antonia Morita Iswari; Stienstra, Ymkje; Subronto, Yanri Wijayanti; Rintiswati, Ning; Sumardi, -; Gerritsen, Jan-Willem; Oord, Henny; Akkerman, Onno W.; Werf, Tjip S. van der

doi:10.1371/journal.pone.0217963

Cited by 29 publications

(20 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the diagnosis can be made within only sixteen minutes, the test can be considered a point-of-care test. Extensive research with the Aeonose has already been done not only in oncology [22][23][24][25] and pre-malignant disorders such as Barrett esophagus [26], but also into infectious diseases such as tuberculosis and in differentiating viral from bacterial respiratory infections in acute COPD exacerbations [27,28].…”

mentioning

confidence: 99%

Applying the electronic nose for pre-operative SARS-CoV-2 screening

et al. 2020

View full text Add to dashboard Cite

Background Infection with SARS-CoV-2 causes corona virus disease (COVID-19). The most standard diagnostic method is reverse transcription-polymerase chain reaction (RT-PCR) on a nasopharyngeal and/or an oropharyngeal swab. The high occurrence of false-negative results due to the non-presence of SARS-CoV-2 in the oropharyngeal environment renders this sampling method not ideal. Therefore, a new sampling device is desirable. This proof-of-principle study investigated the possibility to train machine-learning classifiers with an electronic nose (Aeonose) to differentiate between COVID-19-positive and negative persons based on volatile organic compounds (VOCs) analysis. Methods Between April and June 2020, participants were invited for breath analysis when a swab for RT-PCR was collected. If the RT-PCR resulted negative, the presence of SARS-CoV-2-specific antibodies was checked to confirm the negative result. All participants breathed through the Aeonose for five minutes. This device contains metal-oxide sensors that change in conductivity upon reaction with VOCs in exhaled breath. These conductivity changes are input data for machine learning and used for pattern recognition. The result is a value between − 1 and + 1, indicating the infection probability. Results 219 participants were included, 57 of which COVID-19 positive. A sensitivity of 0.86 and a negative predictive value (NPV) of 0.92 were found. Adding clinical variables to machine-learning classifier via multivariate logistic regression analysis, the NPV improved to 0.96. Conclusions The Aeonose can distinguish COVID-19 positive from negative participants based on VOC patterns in exhaled breath with a high NPV. The Aeonose might be a promising, non-invasive, and low-cost triage tool for excluding SARS-CoV-2 infection in patients elected for surgery.

show abstract

mentioning

confidence: 99%

Applying the electronic nose for pre-operative SARS-CoV-2 screening

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In addition,correct recognition rate (CRR) is also one of indicators to evaluate the performance of iris recognition algorithms. [36] The correspondence between CRR, FAR, and FRR is shown in Formula group 13:…”

Section: Plos Onementioning

confidence: 99%

Constrained unsteady-state iris fast certification for lightweight training samples based on the scale change stable feature and multi-algorithm voting

Liu

Zhu

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

Aiming at the problem of fast certification for a constrained iris in the same category caused by the unstable iris features caused by the change of the iris acquisition environment and shooting status under lightweight training samples, a one-to-one fast certification algorithm for constrained unsteady-state iris based on the scale change stable feature and multi-algorithm voting is proposed. Scale change stable features are found by constructing an isometric differential Gaussian space, and a local binary pattern algorithm with extended statistics (ES-LBP), the Haar wavelet with over threshold detection and the Gabor filter algorithm with immune particle swarm optimization (IPSO) are used to represent the stable features as binary feature codes. Iris certification is performed by the Hamming distance. According to the certification results of three algorithms, the final result is obtained by multi-algorithm voting. Experiments with the JLU and CASIA iris libraries under the iris prerequisite conditions show that the correct recognition rate of this algorithm can reach a high level of 98% or more, indicating that this algorithm can improve the operation speed, accuracy and robustness of certification.

show abstract

“…In asthma, it is used to detect the disease and to determine its phenotype. VOCs are used in cystic fibrosis and in the detection of tuberculosis [18]. Furthermore, nonrespiratory diseases can be detected using an e-nose (e.g.Barrett's oesophagus and inflammatory bowel disease) [8,19].…”

Section: Vocs In Other Diseasesmentioning

confidence: 99%

The electronic nose: emerging biomarkers in lung cancer diagnostics

Geffen

Lamote

Costantini

et al. 2019

Breathe

View full text Add to dashboard Cite

Lung cancer is very common and the most common cause of cancer death worldwide. Despite recent progress in the systemic treatment of lung cancer (checkpoint inhibitors and tyrosine kinase inhibitors), each year, >1.5 million people die due to this disease. Most lung cancer patients already have advanced disease at the time of diagnosis. Computed tomography screening of high-risk individuals can detect lung cancer at an earlier stage but at a cost of false-positive findings. Biomarkers could lead towards a reduction of these false-positive findings and earlier lung cancer diagnosis, and have the potential to improve outcomes and treatment monitoring. To date, there is a lack of such biomarkers for lung cancer and other thoracic malignancies, although electronic nose (e-nose)-derived biomarkers are of interest.E-nose techniques using exhaled breath component measurements can detect lung cancer with a sensitivity ranging from 71% to 96% and specificity from 33 to 100%. In some case series, such results have been validated but this is mostly using internal validation and hence, more work is needed. Furthermore, standardised sampling and analysis methods are lacking, impeding interstudy comparison and clinical implementation. In this narrative review, we provide an overview of the currently available data on E-nose technology for lung cancer detection.

show abstract

Sensitivity and specificity of an electronic nose in diagnosing pulmonary tuberculosis among patients with suspected tuberculosis

Cited by 29 publications

References 34 publications

Applying the electronic nose for pre-operative SARS-CoV-2 screening

Applying the electronic nose for pre-operative SARS-CoV-2 screening

Constrained unsteady-state iris fast certification for lightweight training samples based on the scale change stable feature and multi-algorithm voting

The electronic nose: emerging biomarkers in lung cancer diagnostics

Contact Info

Product

Resources

About