Exhaled volatile organic compounds analysis by e-nose can detect idiopathic pulmonary fibrosis

Electronic nose based analysis of exhaled volatile organic compounds spectrum reveals asthmatic shifts and consistency in controls post-exercise and spirometry

Analyzing exhaled volatile organic compounds (VOCs) with an electronic nose (e-nose) is emerging in medical diagnostics as a non-invasive, quick, and sensitive method for disease detection and monitoring. This study investigates if activities like spirometry or physical exercise affect exhaled VOCs measurements in asthmatics and healthy individuals, a crucial step for e-nose technology's validation for clinical use. The study analyzed exhaled VOCs using an e-nose in 27 healthy individuals and 27 patients with stable asthma, before and after performing spirometry and climbing five flights of stairs. Breath samples were collected using a validated technique and analyzed with a Cyranose 320 e-nose. In healthy controls, the exhaled VOCs spectrum remained unchanged after both lung function test and exercise. In asthmatics, principal component analysis and subsequent discriminant analysis revealed significant differences post-spirometry (vs. baseline 66.7% cross validated accuracy [CVA],p< 0.05) and exercise (vs. baseline 70.4% CVA,p< 0.05). E-nose measurements in healthy individuals are consistent, unaffected by spirometry or physical exercise. However, in asthma patients, significant changes in exhaled VOCs were detected post-activities, indicating airway responses likely due to constriction or inflammation, underscoring the e-nose's potential for respiratory condition diagnosis and monitoring.

Biomarkers in idiopathic pulmonary fibrosis: Current insight and future direction

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive interstitial lung disease with a dismal prognosis. Early diagnosis, accurate prognosis, and personalized therapeutic interventions are essential for improving patient outcomes. Biomarkers, as measurable indicators of biological processes or disease states, hold significant promise in IPF management. In recent years, there has been a growing interest in identifying and validating biomarkers for IPF, encompassing various molecular, imaging, and clinical approaches. This review provides an in-depth examination of the current landscape of IPF biomarker research, highlighting their potential applications in disease diagnosis, prognosis, and treatment response. Additionally, the challenges and future perspectives of biomarker integration into clinical practice for precision medicine in IPF are discussed.

Identification of Idiopathic Pulmonary Fibrosis and Prediction of Disease Severity via Machine Learning Analysis of Comprehensive Metabolic Panel and Complete Blood Count Data

BACKGROUND

Diagnosis of idiopathic pulmonary fibrosis (IPF) typically relies on high-resolution computed tomography imaging (HRCT) or histopathology, while monitoring disease severity is done via frequent pulmonary function testing (PFT). More reliable and convenient methods of diagnosing fibrotic interstitial lung disease (ILD) type and monitoring severity would allow for early identification and enhance current therapeutic interventions. This study tested the hypothesis that a machine learning (ML) ensemble analysis of comprehensive metabolic panel (CMP) and complete blood count (CBC) data can accurately distinguish IPF from connective tissue disease ILD (CTD-ILD) and predict disease severity as seen with PFT.

METHODS

Outpatient data with diagnosis of IPF or CTD-ILD (n = 103 visits by 53 patients) were analyzed via ML methodology to evaluate (1) IPF vs CTD-ILD diagnosis; (2) %predicted Diffusing Capacity of Lung for Carbon Monoxide (DLCO) moderate or mild vs severe; (3) %predicted Forced Vital Capacity (FVC) moderate or mild vs severe; and (4) %predicted FVC mild vs moderate or severe.

RESULTS

ML methodology identified IPF from CTD-ILD with AUCTEST = 0.893, while PFT was classified as DLCO moderate or mild vs severe with AUCTEST = 0.749, FVC moderate or mild vs severe with AUCTEST = 0.741, and FVC mild vs moderate or severe with AUCTEST = 0.739. Key features included albumin, alanine transaminase, %lymphocytes, hemoglobin, %eosinophils, white blood cell count, %monocytes, and %neutrophils.

CONCLUSION

Analysis of CMP and CBC data via proposed ML methodology offers the potential to distinguish IPF from CTD-ILD and predict severity on associated PFT with accuracy that meets or exceeds current clinical practice.

Disease diagnosis and severity classification in pulmonary fibrosis using carbonyl volatile organic compounds in exhaled breath

BACKGROUND

Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs. To explore the potential of exhaled carbonyl VOCs to reflect underlying pathophysiological conditions in pulmonary fibrosis, this proof-of-concept study tested the hypothesis that volatile and low abundance carbonyl compounds could be linked to diagnosis and associated disease severity.

METHODS

Exhaled breath samples were collected from outpatients with a diagnosis of Idiopathic Pulmonary Fibrosis (IPF) or Connective Tissue related Interstitial Lung Disease (CTD-ILD) with stable lung function for 3 months before enrollment, as measured by pulmonary function testing (PFT) DLCO (%), FVC (%) and FEV1 (%). A novel microreactor was used to capture carbonyl compounds in the breath as direct output products. A machine learning workflow was implemented with the captured carbonyl compounds as input features for classification of diagnosis and disease severity based on PFT (DLCO and FVC normal/mild vs. moderate/severe; FEV1 normal/mild/moderate vs. moderately severe/severe).

RESULTS

The proposed approach classified diagnosis with AUROC=0.877 ± 0.047 in the validation subsets. The AUROC was 0.820 ± 0.064, 0.898 ± 0.040, and 0.873 ± 0.051 for disease severity based on DLCO, FEV1, and FVC measurements, respectively. Eleven key carbonyl VOCs were identified with the potential to differentiate diagnosis and to classify severity.

CONCLUSIONS

Exhaled breath carbonyl compounds can be linked to pulmonary function and fibrotic ILD diagnosis, moving towards improved pathophysiological understanding of pulmonary fibrosis.

Differentiating interstitial lung diseases from other respiratory diseases using electronic nose technology

INTRODUCTION

Interstitial lung disease (ILD) may be difficult to distinguish from other respiratory diseases due to overlapping clinical presentation. Recognition of ILD is often late, causing delay which has been associated with worse clinical outcome. Electronic nose (eNose) sensor technology profiles volatile organic compounds in exhaled breath and has potential to detect ILD non-invasively. We assessed the accuracy of differentiating breath profiles of patients with ILD from patients with asthma, chronic obstructive pulmonary disease (COPD), and lung cancer using eNose technology.

METHODS

Patients with ILD, asthma, COPD, and lung cancer, regardless of stage or treatment, were included in a cross-sectional study in two hospitals. Exhaled breath was analysed using an eNose (SpiroNose) and clinical data were collected. Datasets were split in training and test sets for independent validation of the model. Data were analyzed with partial least squares discriminant and receiver operating characteristic analyses.

RESULTS

161 patients with ILD and 161 patients with asthma (n = 65), COPD (n = 50) or lung cancer (n = 46) were included. Breath profiles of patients with ILD differed from all other diseases with an area under the curve (AUC) of 0.99 (95% CI 0.97-1.00) in the test set. Moreover, breath profiles of patients with ILD could be accurately distinguished from the individual diseases with an AUC of 1.00 (95% CI 1.00-1.00) for asthma, AUC of 0.96 (95% CI 0.90-1.00) for COPD, and AUC of 0.98 (95% CI 0.94-1.00) for lung cancer in test sets. Results were similar after excluding patients who never smoked.

CONCLUSIONS

Exhaled breath of patients with ILD can be distinguished accurately from patients with other respiratory diseases using eNose technology. eNose has high potential as an easily accessible point-of-care medical test for identification of ILD amongst patients with respiratory symptoms, and could possibly facilitate earlier referral and diagnosis of patients suspected of ILD.

Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis

Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose disease, as no standard procedure or conclusive test exists. An accurate diagnostic model based on eNose data could therefore be helpful in clinical decision-making. The aim of this paper is to evaluate the performance of various dimensionality reduction methods and classifiers in order to design an accurate diagnostic model for sarcoidosis. Various methods of dimensionality reduction and multiple hyperparameter optimised classifiers were tested and cross-validated on a dataset of patients with pulmonary sarcoidosis (n= 224) and other interstitial lung disease (n= 317). Best performing methods were selected to create a model to diagnose patients with sarcoidosis. Nested cross-validation was applied to calculate the overall diagnostic performance. A classification model with feature selection and random forest (RF) classifier showed the highest accuracy. The overall diagnostic performance resulted in an accuracy of 87.1% and area-under-the-curve of 91.2%. After comparing different dimensionality reduction methods and classifiers, a highly accurate model to diagnose a patient with sarcoidosis using eNose data was created. The RF classifier and feature selection showed the best performance. The presented systematic approach could also be applied to other eNose datasets to compare methods and select the optimal diagnostic model.

Smelling the Disease: Diagnostic Potential of Breath Analysis

Breath analysis is a relatively recent field of research with much promise in scientific and clinical studies. Breath contains endogenously produced volatile organic components (VOCs) resulting from metabolites of ingested precursors, gut and air-passage bacteria, environmental contacts, etc. Numerous recent studies have suggested changes in breath composition during the course of many diseases, and breath analysis may lead to the diagnosis of such diseases. Therefore, it is important to identify the disease-specific variations in the concentration of breath to diagnose the diseases. In this review, we explore methods that are used to detect VOCs in laboratory settings, VOC constituents in exhaled air and other body fluids (e.g., sweat, saliva, skin, urine, blood, fecal matter, vaginal secretions, etc.), VOC identification in various diseases, and recently developed electronic (E)-nose-based sensors to detect VOCs. Identifying such VOCs and applying them as disease-specific biomarkers to obtain accurate, reproducible, and fast disease diagnosis could serve as an alternative to traditional invasive diagnosis methods. However, the success of VOC-based identification of diseases is limited to laboratory settings. Large-scale clinical data are warranted for establishing the robustness of disease diagnosis. Also, to identify specific VOCs associated with illness states, extensive clinical trials must be performed using both analytical instruments and electronic noses equipped with stable and precise sensors.

Novel diagnostic techniques in interstitial lung disease

Research into novel diagnostic techniques and targeted therapeutics in interstitial lung disease (ILD) is moving the field toward increased precision and improved patient outcomes. An array of molecular techniques, machine learning approaches and other innovative methods including electronic nose technology and endobronchial optical coherence tomography are promising tools with potential to increase diagnostic accuracy. This review provides a comprehensive overview of the current evidence regarding evolving diagnostic methods in ILD and to consider their future role in routine clinical care.

Short-Term Effect of Cigarette Smoke on Exhaled Volatile Organic Compounds Profile Analyzed by an Electronic Nose

Breath analysis using an electronic nose (e-nose) is an innovative tool for exhaled volatile organic compound (VOC) analysis, which has shown potential in several respiratory and systemic diseases. It is still unclear whether cigarette smoking can be considered a confounder when analyzing the VOC-profile. We aimed to assess whether an e-nose can discriminate exhaled breath before and after smoking at different time periods. We enrolled 24 healthy smokers and collected their exhaled breath as follows: (a) before smoking, (b) within 5 min after smoking, (c) within 30 min after smoking, and (d) within 60 min after smoking. Exhaled breath was collected by a previously validated method and analyzed by an e-nose (Cyranose 320). By principal component analysis, significant variations in the exhaled VOC profile were shown for principal component 1 and 2 before and after smoking. Significance was higher 30 and 60 min after smoking than 5 min after (p < 0.01 and <0.05, respectively). Canonical discriminant analysis confirmed the above findings (cross-validated values: baseline vs. 5 min = 64.6%, AUC = 0.833; baseline vs. 30 min = 83.6%, AUC = 0.927; baseline vs. 60 min = 89.6%, AUC = 0.933). Thus, the exhaled VOC profile is influenced by very recent smoking. Interestingly, the effect seems to be more closely linked to post-cigarette inflammation than the tobacco-related odorants.

Breath Analysis: A Promising Tool for Disease Diagnosis-The Role of Sensors

Early-stage disease diagnosis is of particular importance for effective patient identification as well as their treatment. Lack of patient compliance for the existing diagnostic methods, however, limits prompt diagnosis, rendering the development of non-invasive diagnostic tools mandatory. One of the most promising non-invasive diagnostic methods that has also attracted great research interest during the last years is breath analysis; the method detects gas-analytes such as exhaled volatile organic compounds (VOCs) and inorganic gases that are considered to be important biomarkers for various disease-types. The diagnostic ability of gas-pattern detection using analytical techniques and especially sensors has been widely discussed in the literature; however, the incorporation of novel nanomaterials in sensor-development has also proved to enhance sensor performance, for both selective and cross-reactive applications. The aim of the first part of this review is to provide an up-to-date overview of the main categories of sensors studied for disease diagnosis applications via the detection of exhaled gas-analytes and to highlight the role of nanomaterials. The second and most novel part of this review concentrates on the remarkable applicability of breath analysis in differential diagnosis, phenotyping, and the staging of several disease-types, which are currently amongst the most pressing challenges in the field.

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

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.

Coating-Based Quartz Crystal Microbalance Detection Methods of Environmentally Relevant Volatile Organic Compounds

Volatile organic compounds (VOCs) that evaporate under standard atmospheric conditions are of growing concern. This is because it is well established that VOCs represent major contamination risks since release of these compounds into the atmosphere can contribute to global warming, and thus, can also be detrimental to the overall health of worldwide populations including plants, animals, and humans. Consequently, the detection, discrimination, and quantification of VOCs have become highly relevant areas of research over the past few decades. One method that has been and continues to be creatively developed for analyses of VOCs is the Quartz Crystal Microbalance (QCM). In this review, we summarize and analyze applications of QCM devices for the development of sensor arrays aimed at the detection of environmentally relevant VOCs. Herein, we also summarize applications of a variety of coatings, e.g., polymers, macrocycles, and ionic liquids that have been used and reported in the literature for surface modification in order to enhance sensing and selective detection of VOCs using quartz crystal resonators (QCRs) and thus QCM. In this review, we also summarize novel electronic systems that have been developed for improved QCM measurements.

Detection of lung cancer with electronic nose using a novel ensemble learning framework

Breath analysis based on electronic nose (e-nose) is a promising new technology for the detection of lung cancer that is non-invasive, simple to operate and cost-effective. Lung cancer screening by e-nose relies on predictive models established using machine learning methods. However, using only a single machine learning method to detect lung cancer has some disadvantages, including low detection accuracy and high false negative rate. To address these problems, groups of individual learning models with excellent performance were selected from classic models, including Support Vector Machine, Decision Tree, Random Forest, Logistic Regression and K-nearest neighbor regression, to build an ensemble learning framework (PCA-SVE). The output result of the PCA-SVE framework was obtained by voting. To test this approach, we analyzed 214 breath samples measured by e-nose with 11 gas sensors of four types using the proposed PCA-SVE framework. Experimental results indicated that the accuracy, sensitivity, and specificity of the proposed framework were 95.75%, 94.78%, and 96.96%, respectively. This framework overcomes the disadvantages of a single model, thereby providing an improved, practical alternative for exhaled breath analysis by e-nose.