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Letter
Ultrafast Screening of COVID-19 by Machine Learning Analysis of Exhaled NO
Wei-Qiao Deng
Shandong University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3671-5951
License:
This work is licensed under a CC BY 4.0 License. Read Full License
A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. Current diagnostic methods, including nucleic acid detection, antibody detection, antigen detection and chest computed tomography (CT) imaging, usually take hours, and identification of the disease costs hundreds of dollars. Therefore, an ultrafast and economical detection method is urgently required to control the epidemic spread. Here, we report a rapid and low-cost method for rapidly preliminary screening COVID-19 suspects from healthy people. We established a machine learning (ML) model based on the fractional exhaled nitric oxide (FeNO) concentration, age, sex and body size of 34 COVID-19 patients and 70 healthy subjects. Then, the model was applied to 45 independent subjects, including 12 mild and asymptomatic COVID-19 patients, 10 patients with other diseases, and 23 healthy subjects. The patients with diseases affecting the FeNO including COVID-19, asthma, hypertension and etc were screened out as suspects with the rate of 94.1%. Only one healthy subject was misclassified. This noninvasive and comfortable detection procedure takes in two minutes and costs less than a dollar, which simultaneously improves the detection efficiency and reduces expenses by multiple orders of magnitude. This work may provide a direction for the control of the rapid spread of COVID-19.
Keywords
COVID-19, machine-learning, fractional exhaled nitric oxide (FeNO), preliminary screening
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Petri Pellinen
commented on 26 September, 2020
I wrote just today in the Finnish Wikipedia that NO levels will rise during infections. Congratulations for your invention!
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This is a preprint. It has not completed peer review.
Letter
Ultrafast Screening of COVID-19 by Machine Learning Analysis of Exhaled NO
Wei-Qiao Deng
Shandong University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-3671-5951
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 25 Sep, 2020
Community comments: 1
Metrics
Comments: 1
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
A new coronavirus, SARS-CoV-2, has caused the coronavirus disease-2019 (COVID-19) epidemic. Current diagnostic methods, including nucleic acid detection, antibody detection, antigen detection and chest computed tomography (CT) imaging, usually take hours, and identification of the disease costs hundreds of dollars. Therefore, an ultrafast and economical detection method is urgently required to control the epidemic spread. Here, we report a rapid and low-cost method for rapidly preliminary screening COVID-19 suspects from healthy people. We established a machine learning (ML) model based on the fractional exhaled nitric oxide (FeNO) concentration, age, sex and body size of 34 COVID-19 patients and 70 healthy subjects. Then, the model was applied to 45 independent subjects, including 12 mild and asymptomatic COVID-19 patients, 10 patients with other diseases, and 23 healthy subjects. The patients with diseases affecting the FeNO including COVID-19, asthma, hypertension and etc were screened out as suspects with the rate of 94.1%. Only one healthy subject was misclassified. This noninvasive and comfortable detection procedure takes in two minutes and costs less than a dollar, which simultaneously improves the detection efficiency and reduces expenses by multiple orders of magnitude. This work may provide a direction for the control of the rapid spread of COVID-19.
Figure 1
Figure 2
Figure 3
Petri Pellinen
commented on 26 September, 2020
I wrote just today in the Finnish Wikipedia that NO levels will rise during infections. Congratulations for your invention!