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Hang W, Bu C, Cui Y, Chen K, Zhang D, Li H, Wang S. Research progress on the pathogenesis and prediction of pneumoconiosis among coal miners. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:319. [PMID: 39012521 DOI: 10.1007/s10653-024-02114-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 07/02/2024] [Indexed: 07/17/2024]
Abstract
Pneumoconiosis is the most common occupational disease among coal miners, which is a lung disease caused by long-term inhalation of coal dust and retention in the lungs. The early stage of this disease is highly insidious, and pulmonary fibrosis may occur in the middle and late stages, leading to an increase in patient pain index and mortality rate. Currently, there is a lack of effective treatment methods. The pathogenesis of pneumoconiosis is complex and has many influencing factors. Although the characteristics of coal dust have been considered the main cause of different mechanisms of pneumoconiosis, the effects of coal dust composition, particle size and shape, and coal dust concentration on the pathogenesis of pneumoconiosis have not been systematically elucidated. Meanwhile, considering the irreversibility of pneumoconiosis progression, early prediction for pneumoconiosis patients is particularly important. However, there is no early prediction standard for pneumoconiosis among coal miners. This review summarizes the relevant research on the pathogenesis and prediction of pneumoconiosis in coal miners in recent years. Firstly, the pathogenesis of coal worker pneumoconiosis and silicosis was discussed, and the impact of coal dust characteristics on pneumoconiosis was analyzed. Then, the early diagnostic methods for pneumoconiosis have been systematically introduced, with a focus on image collaborative computer-aided diagnosis analysis and biomarker detection. Finally, the challenge of early screening technology for miners with pneumoconiosis was proposed.
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Affiliation(s)
- Wenlu Hang
- Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Chunlu Bu
- Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Yuming Cui
- School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Kai Chen
- School of Materials Science and Physics, China University of Mining and Technology, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Dekun Zhang
- School of Materials Science and Physics, China University of Mining and Technology, Xuzhou, 221000, Jiangsu Province, People's Republic of China
| | - Haiquan Li
- Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221000, Jiangsu Province, People's Republic of China.
- School of Chemical Engineering & Technology, China University of Mining and Technology, Xuzhou, 221000, Jiangsu Province, People's Republic of China.
| | - Songquan Wang
- School of Mechatronic Engineering, Jiangsu Normal University, Xuzhou, 221000, Jiangsu Province, People's Republic of China.
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van der Sar IG, Wijsenbeek MS, Braunstahl GJ, Loekabino JO, Dingemans AMC, In 't Veen JCCM, Moor CC. Differentiating interstitial lung diseases from other respiratory diseases using electronic nose technology. Respir Res 2023; 24:271. [PMID: 37932795 PMCID: PMC10626662 DOI: 10.1186/s12931-023-02575-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 10/22/2023] [Indexed: 11/08/2023] Open
Abstract
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.
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Affiliation(s)
- Iris G van der Sar
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Marlies S Wijsenbeek
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Gert-Jan Braunstahl
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Franciscus Gasthuis & Vlietland, Center of Excellence for Asthma, COPD, and Respiratory Allergy, Rotterdam, The Netherlands
| | - Jason O Loekabino
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Anne-Marie C Dingemans
- Department of Respiratory Medicine, Erasmus MC Cancer Institute, University Medical Center, Rotterdam, The Netherlands
| | - Johannes C C M In 't Veen
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands
- Department of Respiratory Medicine, Franciscus Gasthuis & Vlietland, Center of Excellence for Asthma, COPD, and Respiratory Allergy, Rotterdam, The Netherlands
| | - Catharina C Moor
- Department of Respiratory Medicine, Center of Excellence for Interstitial Lung Disease, Erasmus University Medical Center, Rotterdam, The Netherlands.
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van der Sar IG, van Jaarsveld N, Spiekerman IA, Toxopeus FJ, Langens QL, Wijsenbeek MS, Dauwels J, Moor CC. Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis. J Breath Res 2023; 17:047104. [PMID: 37595574 DOI: 10.1088/1752-7163/acf1bf] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Accepted: 08/18/2023] [Indexed: 08/20/2023]
Abstract
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.
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Affiliation(s)
- Iris G van der Sar
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Nynke van Jaarsveld
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Imme A Spiekerman
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Floor J Toxopeus
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Quint L Langens
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Marlies S Wijsenbeek
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Justin Dauwels
- Department of Microelectronics, Delft University of Technology, Delft, The Netherlands
| | - Catharina C Moor
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
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Xuan W, Zheng L, Bunes BR, Crane N, Zhou F, Zang L. Engineering solutions to breath tests based on an e-nose system for silicosis screening and early detection in miners. J Breath Res 2022; 16. [PMID: 35303733 DOI: 10.1088/1752-7163/ac5f13] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/18/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVES This study aims to develop an engineering solution to breath tests using an electronic nose (e-nose), and evaluate its diagnosis accuracy for silicosis. Influencing factors of this technique were explored. METHODS 398 non-silicosis miners and 221 silicosis miners were enrolled in this cross-sectional study. Exhaled breath was analyzed by an array of 16 organic nanofiber sensors along with a customized sample processing system. Principal Component Analysis was used to visualize the breath data, and classifiers were trained by two improved cost-sensitive ensemble algorithms (RF and XGBoost) and two classical algorithms (KNN and SVM). All subjects were included to train the screening model, and an early detection model was run with silicosis cases in stage I. Both 5-fold cross-validation and external validation were adopted. Difference in classifiers caused by algorithms and subjects was quantified using a two-factor analysis of variance. The association between personal smoking habits and classification was investigated by the chi-square test. RESULTS Classifiers of ensemble learning performed well in both screening and early detection model, with an accuracy range of 0.817 to 0.987. Classical classifiers showed relatively worse performance. Besides, the ensemble algorithm type and silicosis cases inclusion had no significant effect on classification (p>0.05). There was no connection between personal smoking habits and classification accuracy. CONCLUSION Breath tests based on an e-nose consisted of 16x sensor array performed well in silicosis screening and early detection. Raw data input showed a more significant effect on classification compared with the algorithm. Personal smoking habits had little impact on models, supporting the applicability of models in large-scale silicosis screening. The e-nose technique and the breath analysis methods reported are expected to provide a quick and accurate screening for silicosis, and extensible for other diseases.
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Affiliation(s)
- Wufan Xuan
- China University of Mining and Technology, School of Safety Engineering, Xuzhou, 221116, CHINA
| | - Lina Zheng
- China University of Mining and Technology, School of Safety Engineering, Xuzhou, 221116, CHINA
| | - Benjamin R Bunes
- Vaporsens, Inc, 419 Wakara Way, Salt Lake City, Utah, 84108, UNITED STATES
| | - Nichole Crane
- Vaporsens, Inc, 419 Wakara Way, Salt Lake City, Utah, UT 84108, UNITED STATES
| | - Fubao Zhou
- China University of Mining and Technology, School of Safety Engineering, Xuzhou, 221116, CHINA
| | - Ling Zang
- Nano Institute of Utah, 36 South Wasatch Drive, Salt Lake City, Utah, 84112-8924, UNITED STATES
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Wojnowski W, Kalinowska K. Machine Learning and Electronic Noses for Medical Diagnostics. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2023]
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Yang HY, Chen WC, Tsai RC. Accuracy of the Electronic Nose Breath Tests in Clinical Application: A Systematic Review and Meta-Analysis. BIOSENSORS 2021; 11:bios11110469. [PMID: 34821685 PMCID: PMC8615633 DOI: 10.3390/bios11110469] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 11/20/2021] [Accepted: 11/20/2021] [Indexed: 05/25/2023]
Abstract
(1) Background: An electronic nose applies a sensor array to detect volatile biomarkers in exhaled breath to diagnose diseases. The overall diagnostic accuracy remains unknown. The objective of this review was to provide an estimate of the diagnostic accuracy of sensor-based breath tests for the diagnosis of diseases. (2) Methods: We searched the PubMed and Web of Science databases for studies published between 1 January 2010 and 14 October 2021. The search was limited to human studies published in the English language. Clinical trials were not included in this review. (3) Results: Of the 2418 records identified, 44 publications were eligible, and 5728 patients were included in the final analyses. The pooled sensitivity was 90.0% (95% CI, 86.3-92.8%, I2 = 47.7%), the specificity was 88.4% (95% CI, 87.1-89.5%, I2 = 81.4%), and the pooled area under the curve was 0.93 (95% CI 0.91-0.95). (4) Conclusion: The findings of our review suggest that a standardized report of diagnostic accuracy and a report of the accuracy in a test set are needed. Sensor array systems of electronic noses have the potential for noninvasiveness at the point-of-care in hospitals. Nevertheless, the procedure for reporting the accuracy of a diagnostic test must be standardized.
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Affiliation(s)
- Hsiao-Yu Yang
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei 10055, Taiwan; (W.-C.C.); (R.-C.T.)
- Department of Public Health, National Taiwan University College of Public Health, Taipei 10055, Taiwan
- Department of Environmental and Occupational Medicine, National Taiwan University Hospital, Taipei 10002, Taiwan
| | - Wan-Chin Chen
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei 10055, Taiwan; (W.-C.C.); (R.-C.T.)
- Department of Family Medicine, Changhua Christian Hospital, Changhua 50006, Taiwan
| | - Rodger-Chen Tsai
- Institute of Environmental and Occupational Health Sciences, National Taiwan University, Taipei 10055, Taiwan; (W.-C.C.); (R.-C.T.)
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van der Sar IG, Wijbenga N, Nakshbandi G, Aerts JGJV, Manintveld OC, Wijsenbeek MS, Hellemons ME, Moor CC. The smell of lung disease: a review of the current status of electronic nose technology. Respir Res 2021; 22:246. [PMID: 34535144 PMCID: PMC8448171 DOI: 10.1186/s12931-021-01835-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 08/26/2021] [Indexed: 02/08/2023] Open
Abstract
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.
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Affiliation(s)
- I G van der Sar
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - N Wijbenga
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - G Nakshbandi
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - J G J V Aerts
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - O C Manintveld
- Department of Cardiology, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - M S Wijsenbeek
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - M E Hellemons
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands
| | - C C Moor
- Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, The Netherlands.
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Machine Learning and Electronic Noses for Medical Diagnostics. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_329-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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A novel lung alveolar cell model for exploring volatile biomarkers of particle-induced lung injury. Sci Rep 2020; 10:15700. [PMID: 32973288 PMCID: PMC7515894 DOI: 10.1038/s41598-020-72825-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 09/03/2020] [Indexed: 11/08/2022] Open
Abstract
Quartz can increase oxidative stress, lipid peroxidation, and inflammation. The objective of this study was to explore the volatile biomarkers of quartz-induced lung injury using a lung alveolar cell model. We exposed the human alveolar A549 cell line to 0, 200, and 500 μg/mL quartz particles for 24 h and used gas chromatography-mass spectrometry to measure the volatile metabolites in the headspace air of cells. We identified ten volatile metabolites that had concentration-response relationships with particles exposure, including 1,2,4-oxadiazole, 5-(4-nitrophenyl)-3-phenyl- (CAS: 28825-12-9), 2,6-dimethyl-6-trifluoroacetoxyoctane (CAS: 61986-67-2), 3-buten-1-amine, N,N-dimethyl- (CAS: 55831-89-5), 2-propanol, 2-methyl- (CAS: 75-65-0), glycolaldehyde dimethyl acetal (CAS: 30934-97-5), propanoic acid, 2-oxo-, ethyl ester (CAS: 617-35-6), octane (CAS: 111-65-9), octane, 3,3-dimethyl- (CAS: 4110-44-5), heptane, 2,3-dimethyl- (CAS: 3074-71-3) and ethanedioic acid, bis(trimethylsilyl) ester (CAS: 18294-04-7). The volatile biomarkers are generated through the pathways of propanoate and nitrogen metabolism. The volatile biomarkers of the alkanes and methylated alkanes are related to oxidative and lipid peroxidation of the cell membrane. The lung alveolar cell model has the potential to explore the volatile biomarkers of particulate-induced lung injury.
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Hashoul D, Haick H. Sensors for detecting pulmonary diseases from exhaled breath. Eur Respir Rev 2019; 28:28/152/190011. [PMID: 31243097 PMCID: PMC9489036 DOI: 10.1183/16000617.0011-2019] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 05/13/2019] [Indexed: 01/09/2023] Open
Abstract
This review presents and discusses a new frontier for fast, risk-free and potentially inexpensive diagnostics of respiratory diseases by detecting volatile organic compounds (VOCs) present in exhaled breath. One part of the review is a didactic presentation of the overlaying concept and the chemistry of exhaled breath. The other part discusses diverse sensors that have been developed and used for the detection of respiratory diseases (e.g. chronic obstructive pulmonary disease, asthma, lung cancer, pulmonary arterial hypertension, tuberculosis, cystic fibrosis, obstructive sleep apnoea syndrome and pneumoconiosis) by analysis of VOCs in exhaled breath. The strengths and pitfalls are discussed and criticised, particularly in the perspective in disseminating information regarding these advances. Ideas regarding the improvement of sensors, sensor arrays, sensing devices and the further planning of workflow are also discussed. Detection of volatile organic compounds from exhaled breath by nanomaterial-based sensors is a new diagnostics frontier in the screening of pulmonary diseases.http://bit.ly/2JoBKXn
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Affiliation(s)
- Dina Hashoul
- Dept of Chemical Engineering, Russell Berrie Nanotechnology Institute, and the Technion Integrated Cancer Center, Haifa, Israel
| | - Hossam Haick
- Dept of Chemical Engineering, Russell Berrie Nanotechnology Institute, and the Technion Integrated Cancer Center, Haifa, Israel
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