Volume 14 - Journal of Control, Vol. 14, No. 5, Special Issue on COVID-19                   JoC 2021, 14 - Journal of Control, Vol. 14, No. 5, Special Issue on COVID-19: 131-140 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Mehralian S, Jalaeian Zaferani E, Shashaani S, Kashefinishabouri F, Teshnehlab M, Sokhandan H A, et al . Rapid COVID-19 Screening Based on the Blood Test using Artificial Intelligence Methods. JoC. 2021; 14 (S1) :131-140
URL: http://joc.kntu.ac.ir/article-1-845-en.html
1- Intelligent Systems Lab., Electrical & Computer Eng. Faculty of K. N. Toosi University of Technology, Tehran, Iran
2- BMI hospital, Tehran, Iran
3- Department of genetics and molecular medicine, Zanjan University of Medical Sciences, Zanjan
Abstract:   (2796 Views)
Coronavirus Disease 2019 (COVID-19) caused by the SARS-CoV-2 virus is spreading rapidly worldwide and has led to widespread deaths globally. As a result, the early diagnosis of patients with COVID-19 is vital to control this dangerous virus's release. There are two common diagnosing methods, chest computed tomography scan (CT-scan) and Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. The most significant disadvantages of RT-PCR molecular tests are the high cost and the long waiting time for test results. The common weaknesses of chest CT-scan are the need for a radiologist to analyze, a misdiagnosis of flu disease due to its similarity, and risky for pregnancy and infants. This article presents a low-cost, highly available method for early detection of COVID-19 based on Artificial Intelligence (AI) systems and blood tests. In this study, 6635 patient's blood tests are used. Experiments conducted using three machine learning algorithms. The results show that the proposed method can detect COVID-19 with an accuracy of %84 and an F1-score of %83. The trained model is being used in a real-world product through an online website called CODAS.
Full-Text [PDF 961 kb]   (777 Downloads)    
Type of Article: Review paper | Subject: COVID-19
Received: 2021/01/29 | Accepted: 2021/03/7 | Published: 2021/02/28

1. T. Acter, N. Uddin, J. Das, A. Akhter, T. R. Choudhury, and S. Kim, "Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency," Science of the Total Environment, p. 138996, 2020. [DOI:10.1016/j.scitotenv.2020.138996]
2. C.-C. Lai, T.-P. Shih, W.-C. Ko, H.-J. Tang, and P.-R. Hsueh, "Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges," International journal of antimicrobial agents, p. 105924, 2020. [DOI:10.1016/j.ijantimicag.2020.105924]
3. P. MARKOWICZ et al., "Multicenter prospective study of ventilator-associated pneumonia during acute respiratory distress syndrome: incidence, prognosis, and risk factors," American journal of respiratory and critical care medicine, vol. 161, no. 6, pp. 1942-1948, 2000. [DOI:10.1164/ajrccm.161.6.9909122]
4. A. D. Makatsariya et al., "Coronavirus disease (COVID-19) and disseminated intravascular coagulation syndrome," Obstetrics, gynecology and reproduction, vol. 14, no. 2, pp. 123-131, 2020. [DOI:10.17749/2313-7347.132]
5. V. Corman et al., "Detection of a novel human coronavirus by real-time reverse-transcription polymerase chain reaction," Eurosurveillance, vol. 17, no. 39, p. 20285, 2012. [DOI:10.2807/ese.17.39.20285-en]
6. A. T. Xiao, Y. X. Tong, and S. Zhang, "False‐negative of RT‐PCR and prolonged nucleic acid conversion in COVID‐19: rather than recurrence," Journal of medical virology, 2020. [DOI:10.1002/jmv.25855]
7. F. Cabitza et al., "Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests," Clinical Chemistry and Laboratory Medicine (CCLM), vol. 1, no. ahead-of-print, 2020. [DOI:10.1515/cclm-2020-1294]
8. A. Bhandary et al., "Deep-learning framework to detect lung abnormality-A study with chest X-Ray and lung CT scan images," Pattern Recognition Letters, vol. 129, pp. 271-278, 2020. [DOI:10.1016/j.patrec.2019.11.013]
9. H. Khorramdelazad, M. H. Kazemi, A. Najafi, M. Keykhaee, R. Z. Emameh, and R. Falak, "Immunopathological similarities between COVID-19 and influenza: Investigating the consequences of Co-infection," Microbial pathogenesis, p. 104554, 2020. [DOI:10.1016/j.micpath.2020.104554]
10. B. S. Bleier and K. C. Welch, "Preprocedural COVID‐19 screening: Do rhinologic patients carry a unique risk burden for false‐negative results?," in International forum of allergy & rhinology, 2020, vol. 10, no. 10, pp. 1186-1188: Wiley Online Library. [DOI:10.1002/alr.22645]
11. Y. Li and L. Xia, "Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management," American Journal of Roentgenology, vol. 214, no. 6, pp. 1280-1286, 2020. [DOI:10.2214/AJR.20.22954]
12. J. Vernarelli and J. Lambert, "Flavonoid intake is inversely associated with obesity and C-reactive protein, a marker for inflammation, in US adults," Nutrition & diabetes, vol. 7, no. 5, pp. e276-e276, 2017. [DOI:10.1038/nutd.2017.22]
13. W. Ling, "C-reactive protein levels in the early stage of COVID-19," Medecine et maladies infectieuses, 2020.
14. J. J. Deeks et al., "Antibody tests for identification of current and past infection with SARS‐CoV‐2," Cochrane Database of Systematic Reviews, no. 6, 2020. [DOI:10.1002/14651858.CD013652]
15. "World Health Organization."
16. T. Struyf et al., "Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID‐19 disease," Cochrane Database of Systematic Reviews, no. 7, 2020. [DOI:10.1002/14651858.CD013665]
17. X. Mei et al., "Artificial intelligence-enabled rapid diagnosis of patients with COVID-19," Nature Medicine, pp. 1-5, 2020.
18. Y. Xing, P. Mo, Y. Xiao, O. Zhao, Y. Zhang, and F. Wang, "Post-discharge surveillance and positive virus detection in two medical staff recovered from coronavirus disease 2019 (COVID-19), China, January to February 2020," Eurosurveillance, vol. 25, no. 10, p. 2000191, 2020. [DOI:10.2807/1560-7917.ES.2020.25.10.2000191]
19. Y. Fang et al., "Sensitivity of chest CT for COVID-19: comparison to RT-PCR," Radiology, p. 200432, 2020. [DOI:10.1148/radiol.2020200432]
20. E. Neri, V. Miele, F. Coppola, and R. Grassi, "Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology," La radiologia medica, p. 1, 2020. [DOI:10.1007/s11547-020-01197-9]
21. S. Wang et al., "A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)," MedRxiv, 2020. [DOI:10.1101/2020.02.14.20023028]
22. D. Brinati, A. Campagner, D. Ferrari, M. Locatelli, G. Banfi, and F. Cabitza, "Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: a Feasibility Study," medRxiv, 2020. [DOI:10.1101/2020.04.22.20075143]
23. M. Zokaeinikoo, P. Kazemian, P. Mitra, and S. Kumara, "AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images," medRxiv, 2020. [DOI:10.1101/2020.05.24.20111922]
24. D. Dong et al., "The role of imaging in the detection and management of COVID-19: a review," IEEE reviews in biomedical engineering, 2020.
25. L. Li et al., "Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT," Radiology, 2020.
26. J. Wu et al., "Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results," medRxiv, 2020. [DOI:10.1101/2020.04.02.20051136]

Add your comments about this article : Your username or Email:

Send email to the article author

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2022 CC BY-NC 4.0 | Journal of Control

Designed & Developed by : Yektaweb