Diagnostic performance of electronic nose technology in chronic lung allograft dysfunction

Wijbenga, Nynke; Hoek, Rogier A.S.; Mathot, Bas J.; Seghers, Leonard; Moor, Catharina C.; Aerts, Joachim; Bos, Daniël; Manintveld, Olivier C.; Hellemons, Merel E.

doi:10.1016/j.healun.2022.09.009

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…With this narrow therapeutic range, eNose technology is not accurate enough for this indication. Nonetheless, eNose technology does have potential in other applications within the field of LTx, such as the detection of chronic rejection after LTx [16].…”

Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of eNose ‘breathprints’ for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation

Wijbenga,

Muller,

Hoek

et al. 2023

J. Breath Res.

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Introduction: In order to prevent long-term immunity-related complications after lung transplantation, close monitoring of immunosuppressant levels using therapeutic drug monitoring (TDM) is paramount. Novel electronic nose (eNose) technology may be a non-invasive alternative to the current invasive procedures for TDM. We investigated the diagnostic and categorization capacity of eNose breathprints for Tacrolimus trough blood plasma levels (TACtrough) in lung transplant recipients (LTR).Method: We performed eNose measurements in stable LTR attending the outpatient clinic. We evaluated 1) the correlation between eNose measurements and TACtrough, 2) the diagnostic capacity of eNose technology for TACtrough, and 3) the accuracy of eNose technology for categorization of TACtrough into three clinically relevant categories (low: <7 µg/mL, medium: 7 – 10 µg/mL, and high: >10 µg/mL).Results: A total of 186 measurements from 86 LTR were included. There was a weak but statistically significant correlation (r = 0.21, p = 0.004) between the eNose measurements and TACtrough. The root mean squared error of prediction (RMSEP) for the diagnostic capacity was 3.186 in the training and 3.131 in the validation set. The accuracy of categorization ranged between 45-63% for the training set and 52-69% in the validation set. Conclusion: There is a weak correlation between eNose breathprints and TACtrough in LTR. However, the diagnostic as well as categorization capacity for TACtrough using eNose breathprints is too inaccurate to be applicable in TDM. 

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Section: Discussionmentioning

confidence: 99%

Diagnostic accuracy of eNose ‘breathprints’ for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation

Wijbenga,

Muller,

Hoek

et al. 2023

J. Breath Res.

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“…PVV, unique to machine learning, was the strongest in phenotyping and prognostication [12 && ]. In a recent study, exhaled breath analysis using an electronic nose (eNose) seemed to be a promising novel tool for enabling diagnosis and phenotyping CLAD and could therefore be a valuable addition to the diagnostic armamentarium for suspected graft failure in lung transplant recipients, although further studies are definitely needed [13].…”

Section: Diagnosis Of Chronic Lung Allograft Dysfunctionmentioning

confidence: 99%

The diagnosis and management of chronic lung allograft dysfunction

Verleden,

Hendriks,

Verleden

2024

Current Opinion in Pulmonary Medicine

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Purpose of review Chronic lung allograft dysfunction (CLAD) remains a life-threatening complication following lung transplantation. Different CLAD phenotypes have recently been defined, based on the combination of pulmonary function testing and chest computed tomography (CT) scanning and spurred renewed interests in differential diagnosis, risk factors and management of CLAD. Recent findings Given their crucial importance in the differential diagnosis, we will discuss the latest development in assessing the pulmonary function and chest CT scan, but also their limitations in proper CLAD phenotyping, especially with regards to patients with baseline allograft dysfunction. Since no definitive treatment exists, it remains important to timely identify clinical risk factors, but also to assess the presence of specific patterns or biomarkers in tissue or in broncho alveolar lavage in relation to CLAD (phenotypes). We will provide a comprehensive overview of the latest advances in risk factors and biomarker research in CLAD. Lastly, we will also review novel preventive and curative treatment strategies for CLAD. Summary Although this knowledge has significantly advanced the field of lung transplantation, more research is warranted because CLAD remains a life-threatening complication for all lung transplant recipients.

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“…Reducing the time spent on medical diagnosis provides more time to medical doctors to concentrate on professional surgery and consultation and thus leveraging the healthcare quality. In this paper, we consider the traditional lung cancer screening via biomedical imaging, instead of an emerging approach using breath by the electronic nose [4,5].…”

Section: Introductionmentioning

confidence: 99%

Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection

Chui¹,

Gupta

Jhaveri

et al. 2023

International Journal of Intelligent Systems

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Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an accurate LCD model are the small-scale datasets, the poor generalizability to detect unseen data, and the selection of useful source domains and prioritization of multiple source domains for transfer learning. In this paper, a multiround transfer learning and modified generative adversarial network (MTL-MGAN) algorithm is proposed for LCD. The MTL transfers the knowledge between the prioritized source domains and target domain to get rid of exhaust search of datasets prioritization among multiple datasets, maximizing the transferability with a multiround transfer learning process, and avoiding negative transfer via customization of loss functions in the aspects of domain, instance, and feature. In regard to the MGAN, it not only generates additional training data but also creates intermediate domains to bridge the gap between the source domains and target domains. 10 benchmark datasets are chosen for the performance evaluation and analysis of the MTL-MGAN. The proposed algorithm has significantly improved the accuracy compared with related works. To examine the contributions of the individual components of the MTL-MGAN, ablation studies are conducted to confirm the effectiveness of the prioritization algorithm, the MTL, the negative transfer avoidance via loss functions, and the MGAN. The research implications are to confirm the feasibility of multiround transfer learning to enhance the optimal solution of the target model and to provide a generic approach to bridge the gap between the source domain and target domain using MGAN.

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Diagnostic performance of electronic nose technology in chronic lung allograft dysfunction

Cited by 6 publications

References 39 publications

Diagnostic accuracy of eNose ‘breathprints’ for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation

Diagnostic accuracy of eNose ‘breathprints’ for therapeutic drug monitoring of Tacrolimus trough levels in lung transplantation

The diagnosis and management of chronic lung allograft dysfunction

Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection

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