Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition
The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by util...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Research
2022
|
| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/284234/1/Nurputra_KKMK.pdf |
| _version_ | 1826050767077769216 |
|---|---|
| author | Nurputra, Dian Kesumapramudya Kusumaatmaja, Ahmad Hakim, Mohamad Saifudin Hidayat, Shidiq Nur Julian, Trisna Sumanto, Budi Mahendradhata, Yodi Saktiawati, Antonia Morita Iswari Wasisto, Hutomo Suryo Triyana, Kuwat |
| author_facet | Nurputra, Dian Kesumapramudya Kusumaatmaja, Ahmad Hakim, Mohamad Saifudin Hidayat, Shidiq Nur Julian, Trisna Sumanto, Budi Mahendradhata, Yodi Saktiawati, Antonia Morita Iswari Wasisto, Hutomo Suryo Triyana, Kuwat |
| author_sort | Nurputra, Dian Kesumapramudya |
| collection | UGM |
| description | The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening. |
| first_indexed | 2024-03-14T00:09:45Z |
| format | Article |
| id | oai:generic.eprints.org:284234 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:09:45Z |
| publishDate | 2022 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2842342023-12-01T03:00:25Z https://repository.ugm.ac.id/284234/ Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition Nurputra, Dian Kesumapramudya Kusumaatmaja, Ahmad Hakim, Mohamad Saifudin Hidayat, Shidiq Nur Julian, Trisna Sumanto, Budi Mahendradhata, Yodi Saktiawati, Antonia Morita Iswari Wasisto, Hutomo Suryo Triyana, Kuwat Community Child Health Public Health and Health Services The reverse transcription-quantitative polymerase chain reaction (RT-qPCR) approach has been widely used to detect the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, instead of using it alone, clinicians often prefer to diagnose the coronavirus disease 2019 (COVID-19) by utilizing a combination of clinical signs and symptoms, laboratory test, imaging measurement (e.g., chest computed tomography scan), and multivariable clinical prediction models, including the electronic nose. Here, we report on the development and use of a low cost, noninvasive method to rapidly sniff out COVID-19 based on a portable electronic nose (GeNose C19) integrating an array of metal oxide semiconductor gas sensors, optimized feature extraction, and machine learning models. This approach was evaluated in profiling tests involving a total of 615 breath samples composed of 333 positive and 282 negative samples. The samples were obtained from 43 positive and 40 negative COVID-19 patients, respectively, and confirmed with RT-qPCR at two hospitals located in the Special Region of Yogyakarta, Indonesia. Four different machine learning algorithms (i.e., linear discriminant analysis, support vector machine, stacked multilayer perceptron, and deep neural network) were utilized to identify the top-performing pattern recognition methods and to obtain a high system detection accuracy (88–95%), sensitivity (86–94%), and specificity (88–95%) levels from the testing datasets. Our results suggest that GeNose C19 can be considered a highly potential breathalyzer for fast COVID-19 screening. Nature Research 2022-08-16 Article PeerReviewed application/pdf en https://repository.ugm.ac.id/284234/1/Nurputra_KKMK.pdf Nurputra, Dian Kesumapramudya and Kusumaatmaja, Ahmad and Hakim, Mohamad Saifudin and Hidayat, Shidiq Nur and Julian, Trisna and Sumanto, Budi and Mahendradhata, Yodi and Saktiawati, Antonia Morita Iswari and Wasisto, Hutomo Suryo and Triyana, Kuwat (2022) Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition. npj Digital Medicine, 5 (115). pp. 1-17. ISSN 23986352 https://www.nature.com/articles/s41746-022-00661-2 https://doi.org/10.1038/s41746-022-00661-2 |
| spellingShingle | Community Child Health Public Health and Health Services Nurputra, Dian Kesumapramudya Kusumaatmaja, Ahmad Hakim, Mohamad Saifudin Hidayat, Shidiq Nur Julian, Trisna Sumanto, Budi Mahendradhata, Yodi Saktiawati, Antonia Morita Iswari Wasisto, Hutomo Suryo Triyana, Kuwat Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition |
| title | Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition |
| title_full | Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition |
| title_fullStr | Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition |
| title_full_unstemmed | Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition |
| title_short | Fast and noninvasive electronic nose for sniffing out COVID-19 based on exhaled breath-print recognition |
| title_sort | fast and noninvasive electronic nose for sniffing out covid 19 based on exhaled breath print recognition |
| topic | Community Child Health Public Health and Health Services |
| url | https://repository.ugm.ac.id/284234/1/Nurputra_KKMK.pdf |
| work_keys_str_mv | AT nurputradiankesumapramudya fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT kusumaatmajaahmad fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT hakimmohamadsaifudin fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT hidayatshidiqnur fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT juliantrisna fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT sumantobudi fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT mahendradhatayodi fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT saktiawatiantoniamoritaiswari fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT wasistohutomosuryo fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition AT triyanakuwat fastandnoninvasiveelectronicnoseforsniffingoutcovid19basedonexhaledbreathprintrecognition |