Skip to main content

COVID-19 Diagnostic Approaches: An Overview

  • Conference paper
  • First Online:
CMBEBIH 2021 (CMBEBIH 2021)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 84))

Included in the following conference series:

  • 883 Accesses

Abstract

In the midst of the global COVID-19 pandemic, diagnostic approaches have played a critical role in tracking and fighting the virus. In the light of this, the state-of-the-art biosensing devices and procedures being repurposed for COVID-19 diagnosis are critically evaluated. Various COVID-19 diagnostic approaches are constantly being tested and optimized in order to ensure accurate diagnosis and reduce false results. This review spotlights diagnostic strategies such as: (1) Reverse transcription polymerase chain reaction (RT-PCR), (2) Enzyme-linked immunosorbent test (ELISA), (3) Chemiluminescent Immunoassay (CLIA), (4) Lateral flow immunoassay (LFIA), (5) Clustered Regularly Interspersed Palindromic Repeats (CRISPR-Cas13), (6) Clustered Regularly Interspersed Palindromic Repeats (CRISPR-Cas12a), (7) Loop-mediated Isothermal Amplification (LAMP-based colorimetric method), (8) Point-of-care tests (POC- RT-PCR), (9) Point-of-care tests (POC- Isothermal DNA amplification), (10) reactive oxygen species (ROS), (11) Chest imaging using computerized tomography (CT), (12) X-ray radiography (CXR), (13) Field-effect transistor (FET) in order to address different biomarkers correlated with COVID-19. The overview of these diagnostic approaches is given in terms of their strengths, limitations, time per analysis, costs, and availability. Finally, conclusions on diagnostic tests are highlighted.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., Xia, L.: Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (covid-19) in china: a report of 1014 cases. Radiology 296(2), E32–E40 (2020)

    Article  Google Scholar 

  2. Rada, G., Verdugo-Paiva, F., Avila, C., Morel-Marambio, M., Bravo-Jeria, R., Pesce, F., Madrid, E., Izcovich, A.: Evidence synthesis relevant to COVID-19: a protocol for multiple systematic reviews and overviews of systematic reviews. Medwave 220(3), e7868 (2020)

    Google Scholar 

  3. Vellingiri, B., Jayaramayya, K., Iyer, M., Narayanasamy, A., Govindasamy, V., Giridharan, B., Ganesan, S., Venugopal, A., Venkatesan, D., Ganesan, H., et al.: COVID-19: a promising cure for the global panic. Sci. Total Environ. 825, 138277 (2020)

    Article  Google Scholar 

  4. Saif, L.: Animal coronaviruses: what can they teach us about the severe acute respiratory syndrome? Revue scientifique et technique-Office international des epizooties 23(2), 643–660 (2004)

    Article  Google Scholar 

  5. Jiang, F., Deng, L., Zhang, L., Cai, Y., Cheung, C.W., Xia, Z.: Review of the clinical characteristics of coronavirus disease 2019 (COVID-19). J. Gen. Intern. Med. 4, 1–5 (2020)

    Google Scholar 

  6. Salzberger, B., Buder, F., Lampl, B., Ehrenstein, B., Hitzenbichler, F., Holzmann, T., Schmidt, B., Hanses, F.: Epidemiology of SARS-CoV-2. Infection 8, 1–7 (2020)

    Google Scholar 

  7. Dashraath, P., Jeslyn, W.J., Karen, L.M., Min, L.L., Sarah, L., Biswas, A., Choolani, M.A., Mattar, C., Lin, S.L.: Coronavirus disease 2019 (COVID-19) pandemic and pregnancy. Am. J. Obstet. Gynecol. 222(6), 521–531 (2020)

    Article  Google Scholar 

  8. World Health Organization: Coronavirus disease (COVID-19): weekly epidemiological, update 1 (2020)

    Google Scholar 

  9. Mahmoudi, M.: Emerging biomolecular testing to assess risk of mortality from COVID-19 infection. Mol. Pharm. 18(2), 476–482 (2020)

    Article  Google Scholar 

  10. Mehta, P., McAuley, D.F., Brown, M., Sanchez, E., Tattersall, R.S., Manson, J.J., Collaboration, H.A.S., et al.: COVID-19: consider cytokine storm syndromes and immunosuppression. Lancet (London, England) 395(10229), 1033 (2020)

    Article  Google Scholar 

  11. Ruan, Q., Yang, K., Wang, W., Jiang, L., Song, J.: Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Med. 46(5), 846–848 (2020)

    Article  Google Scholar 

  12. Gruenwald, H.: Covid-19 and vaccination

    Google Scholar 

  13. Tang, Y.-W., Schmitz, J.E., Persing, D.H., Stratton, C.W.: Laboratory diagnosis of COVID19: current issues and challenges. J. Clin. Microbiol. 58(6), e00512–e00520 (2020)

    Article  Google Scholar 

  14. Qiu, G., Gai, Z., Tao, Y., Schmitt, J., Kullak-Ublick, G.A., Wang, J.: Dual-functional plasmonic photothermal biosensors for highly accurate severe acute respiratory syndrome coronavirus 2 detection. ACS Nano 14(5), 5268–5277 (2020)

    Article  Google Scholar 

  15. Santiago, I.: Trends and innovations in biosensors for COVID-19 mass testing. ChemBioChem 21, 1–11 (2020)

    Article  Google Scholar 

  16. Morales-Narvaez, E., Dincer, C.: The impact of biosensing in a pandemic outbreak: COVID-19. Biosens. Bioelectron. 163, 112274 (2020)

    Article  Google Scholar 

  17. Zhang, F., Abudayyeh, O.O., Gootenberg, J.S.: A protocol for detection of covid-19 using crispr diagnostics (2020)

    Google Scholar 

  18. Cui, F., Zhou, H.S.: Diagnostic methods and potential portable biosensors for coronavirus disease 2019. Biosens. Bioelectron. 165, 112349 (2020)

    Article  Google Scholar 

  19. Ravi, N., Cortade, D.L., Ng, E., Wang, S.X.: Diagnostics for SARS-CoV-2 detection: a comprehensive review of the FDA-EUA COVID-19 testing landscape. Biosens. Bioelectron. 1(165), 112454 (2020)

    Article  Google Scholar 

  20. Tantuoyir, M.M., Rezaei, N.: Serological tests for COVID-19: potential opportunities. Cell Biol. Int. 45(4), 740–748 (2020)

    Article  Google Scholar 

  21. Touma, M.: COVID-19: molecular diagnostics overview. J. Mol. Med. 98(7), 947–54 (2020)

    Article  Google Scholar 

  22. Sapkal, G., Shete-Aich, A., Jain, R., Yadav, P.D., Sarkale, P., Lakra, R., Baradkar, S., Deshpande, G.R., Mali, D., Tilekar, B.N., Majumdar, T.: Development of indigenous IgG ELISA for the detection of anti-SARS-CoV-2 IgG. Indian J. Med. Res. 151(5), 444 (2020)

    Article  Google Scholar 

  23. Zhang, L., Guo, H.: Biomarkers of COVID-19 and technologies to combat SARS-CoV-2. Adv. Biomark. Sci. Technol. 2, 1–23 (2020)

    Article  Google Scholar 

  24. Weissleder, R., Lee, H., Ko, J., Pittet, M.J.: COVID-19 diagnostics in context. Sci. Transl. Med. 12(546), eabc1931 (2020)

    Article  Google Scholar 

  25. Infantino, M., Grossi, V., Lari, B., Bambi, R., Perri, A., Manneschi, M., Terenzi, G., Liotti, I., Ciotta, G., Taddei, C., Benucci, M.: Diagnostic accuracy of an automated chemiluminescent immunoassay for anti-SARS-CoV-2 IgM and IgG antibodies: an Italian experience. J. Med. Virol. 92(9), 1671–5 (2020)

    Article  Google Scholar 

  26. Xiao, Q., Xu, C.: Research progress on chemiluminescence immunoassay combined with novel technologies. TrAC Trends Anal. Chem. 124, 115780 (2020)

    Article  Google Scholar 

  27. Borse, V.B., Konwar, A.N., Jayant, R.D., Patil, P.O.: Perspectives of characterization and bioconjugation of gold nanoparticles and their application in lateral flow immunosensing. Drug Deliv. Transl. Res. 10(4), 878–902 (2020)

    Article  Google Scholar 

  28. Koczula, K.M., Gallotta, A.: Lateral flow assays. Essays Biochem. 60(1), 111–20 (2016)

    Article  Google Scholar 

  29. Broughton, J.P., Deng, X., Yu, G., Fasching, C.L., Servellita, V., Singh, J., Miao, X., Streithorst, J.A., Granados, A., Sotomayor-Gonzalez, A., et al.: CRISPR–Cas12-based detection of SARS-CoV-2. Nat. Biotechnol. 38(7), 870–874 (2020)

    Article  Google Scholar 

  30. Nicol, T., Lefeuvre, C., Serri, O., Pivert, A., Joubaud, F., Dubée, V., Kouatchet, A., Ducancelle, A., Lunel-Fabiani, F., Le Guillou-Guillemette, H.: Assessment of SARS-CoV-2 serological tests for the diagnosis of COVID-19 through the evaluation of three immunoassays: two automated immunoassays (Euroimmun and Abbott) and one rapid lateral flow immunoassay (NG Biotech). J. Clin. Virol. 129, 104511 (2020)

    Article  Google Scholar 

  31. East-Seletsky, A., O’Connell, M.R., Knight, S.C., Burstein, D., Cate, J.H., Tjian, R., Doudna, J.A.: Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection. Nature 538(7624), 270–273 (2016)

    Article  Google Scholar 

  32. Benzigar, M.R., Bhattacharjee, R., Baharfar, M., Liu, G.: Current methods for diagnosis of human coronaviruses: pros and cons. Anal. Bioanal. Chem. 20, 1–20 (2020)

    Google Scholar 

  33. Lamb, L.E., Bartolone, S.N., Ward, E., Chancellor, M.B.: Rapid detection of novel coronavirus (COVID19) by reverse transcription-loop-mediated isothermal amplification. Available at SSRN 3539654 (2020)

    Google Scholar 

  34. Pyrc, K., Milewska, A., Potempa, J.: Development of loop-mediated isothermal amplification assay for detection of human coronavirus-NL63. J. Virol. Methods 175(1), 133–136 (2011)

    Article  Google Scholar 

  35. Thai, H.T., Le, M.Q., Vuong, C.D., Parida, M., Minekawa, H., Notomi, T., Hasebe, F., Morita, K.: Development and evaluation of a novel loop-mediated isothermal amplification method for rapid detection of severe acute respiratory syndrome coronavirus. J. Clin. Microbiol. 42(5), 1956–61 (2004)

    Article  Google Scholar 

  36. Augustine, R., Hasan, A., Das, S., Ahmed, R., Mori, Y., Notomi, T., Kevadiya, B.D., Thakor, A.S.: Loop-mediated isothermal amplification (Lamp): a rapid, sensitive, specific, and cost-effective point-of-care test for coronaviruses in the context of COVID-19 pandemic. Biology 9(8), 182 (2020)

    Article  Google Scholar 

  37. Loeffelholz, M.J., Alland, D., Butler-Wu, S.M., Pandey, U., Perno, C.F., Nava, A., Carroll, K.C., Mostafa, H., Davies, E., McEwan, A., Rakeman, J.L.: Multicenter evaluation of the cepheid Xpert Xpress SARS-CoV-2 test. J. Clin. Microbiol. 58(8), e00926-20 (2020)

    Article  Google Scholar 

  38. Huang, W.E., Lim, B., Hsu, C.C., Xiong, D., Wu, W., Yu, Y., Jia, H., Wang, Y., Zeng, Y., Ji, M., Chang, H.: RT-LAMP for rapid diagnosis of coronavirus SARS-CoV-2. Microbial Biotechnol. 13(4), 950–961 (2020)

    Article  Google Scholar 

  39. Diagnostic testing at the speed of life | Cue (2020). https://www.cuehealth.com/#product. Accessed 26 Dec 2020

  40. Miripour, Z.S., Sarrami-Forooshani, R., Sanati, H., Makarem, J., Taheri, M.S., Shojaeian, F., Eskafi, A.H., Abbasvandi, F., Namdar, N., Ghafari, H., Aghaee, P.: Real-time diagnosis of reactive oxygen species (ROS) in fresh sputum by electrochemical tracing; correlation between COVID-19 and viral-induced ROS in lung/respiratory epithelium during this pandemic. Biosens. Bioelectron. 165, 112435 (2020)

    Article  Google Scholar 

  41. Xu, L., Li, D., Ramadan, S., Li, Y., Klein, N.: Facile biosensors for rapid detection of COVID-19. Biosens. Bioelectron. 170, 112673 (2020)

    Article  Google Scholar 

  42. Caldemeyer, K.S., Buckwalter, K.A.: The basic principles of computed tomography and magnetic resonance imaging. J. Am. Acad. Dermatol. 41(5), 768–71 (1999)

    Article  Google Scholar 

  43. Yin, Z., Wu, M., Wu, Z.: Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images. SIViP 7, 1–8 (2021)

    Google Scholar 

  44. Li, A.C., Lee, D.T., Misquitta, K.K., Uno, K., Wald, S.: COVID-19 detection from chest radiographs using machine learning and convolutional neural networks. medRxiv (2020)

    Google Scholar 

  45. Han, Y., Chen, C., Tewfik, A.H., Ding, Y., Peng Y.: Pneumonia detection on chest X-ray using radiomic features and contrastive learning. arXiv preprint arXiv:2101.04269 (2021)

  46. Afifi, A., Hafsa, N.E., Ali, M.A., Alhumam, A., Alsalman, S.: An ensemble of global and local attention based convolutional neural networks for COVID-19 diagnosis on chest X-ray images. Symmetry 13(1), 113 (2021)

    Article  Google Scholar 

  47. Siddiqui, S.Y., Abbas, S., Khan, M.A., Naseer, I., Masood, T., Khan, K.M., Al Ghamdi, M.A., Almotiri, S.H.: Intelligent decision support system for COVID-19 empowered with deep learning. CMC-Comput. Mater. Continua 66(2), 1719–1732 (2021)

    Article  Google Scholar 

  48. Ypsilantis, P.P., Montana, G.: Learning what to look in chest X-rays with a recurrent visual attention model. arXiv preprint arXiv:1701.06452 (2017)

  49. Seo, G., Lee, G., Kim, M.J., Baek, S.-H., Choi, M., Ku, K.B., Lee, C.-S., Jun, S., Park, D., Kim, H.G., et al.: Rapid detection of COVID-19 causative virus (SARS-CoV-2) in human nasopharyngeal swab specimens using field-effect transistor-based biosensor. ACS Nano 14(4), 5135–5142 (2020)

    Article  Google Scholar 

  50. Fda.gov (2020). https://www.fda.gov/media/134922/download. Accessed 26 Dec 2020

  51. Miller, T.E., Garcia Beltran, W.F., Bard, A.Z., Gogakos, T., Anahtar, M.N., Astudillo, M.G., Yang, D., Thierauf, J., Fisch, A.S., Mahowald, G.K., Fitzpatrick, M.J.: Clinical sensitivity and interpretation of PCR and serological COVID-19 diagnostics for patients presenting to the hospital. FASEB J. 34(10), 13877–13884 (2020)

    Article  Google Scholar 

  52. Lionex.de (2021). https://lionex.de/wpcontent/uploads/2020/05/COVID-19-ELISA-Human-IgG_EN_Instructions-for-use-rev.-0.pdf. Accessed 31 Jan 2021]

  53. Open.fda.gov (2021). https://open.fda.gov/apis/device/covid19serology/. Accessed 31 Jan 2021

  54. Bastos, M.L., Tavaziva, G., Abidi, S.K., Campbell, J.R., Haraoui, L.P., Johnston, J.C., Lan, Z., Law, S., MacLean, E., Trajman, A., Menzies, D.: Diagnostic accuracy of serological tests for COVID-19: systematic review and meta-analysis. BMJ 370, m2516 (2020)

    Article  Google Scholar 

  55. Gutiérrez-Cobos, A., de Frutos, S.G., García, D.D., Lara, E.N., Carrión, A.Y., García-Rodrigo, L.F., Torres, A.M., Domingo, L.C.: Evaluation of diagnostic accuracy of 10 serological assays for detection of SARS-CoV-2 antibodies. Eur. J. Clin. Microbiol. Infect. Dis. 24, 1–7 (2020)

    Google Scholar 

  56. Krüttgen, A., Cornelissen, C.G., Dreher, M., Hornef, M., Imöhl, M., Kleines, M.: Comparison of four new commercial serologic assays for determination of SARS-CoV-2 IgG. J. Clin. Virol. 128, 104394 (2020)

    Article  Google Scholar 

  57. Kontou, P.I., Braliou, G.G., Dimou, N.L., Nikolopoulos, G., Bagos, P.G.: Antibody tests in detecting SARS-CoV-2 infection: a meta-analysis. Diagnostics 10(5), 319 (2020)

    Article  Google Scholar 

  58. Vashist, S.K.: In vitro diagnostic assays for COVID-19: recent advances and emerging trends. Diagnostics 10(4), 202 (2020)

    Article  Google Scholar 

  59. Ma, H., Zeng, W., He, H., Zhao, D., Yang, Y., Jiang, D., Zhou, P., Qi, Y., He, W., Zhao, C., Yi, R.: COVID-19 diagnosis and study of serum SARS-CoV-2 specific IgA, IgM and IgG by a quantitative and sensitive immunoassay. MedRxiv (2020)

    Google Scholar 

  60. Soleimani, R., Khourssaji, M., Gruson, D., Rodriguez-Villalobos, H., Berghmans, M., Belkhir, L., Yombi, J.C., Kabamba-Mukadi, B.: Clinical usefulness of fully automated chemiluminescent immunoassay for quantitative antibody measurements in COVID-19 patients. J. Med. Virol. 93(3), 1465–1477 (2020)

    Article  Google Scholar 

  61. Li, Z., Yi, Y., Luo, X., Xiong, N., Liu, Y., Li, S., Sun, R., Wang, Y., Hu, B., Chen, W., Zhang, Y.: Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-infection diagnosis. J. Med. Virol. 92(9), 1518–1524 (2020)

    Article  Google Scholar 

  62. Kubina, R., Dziedzic, A.: Molecular and serological tests for COVID-19 a comparative review of SARS-CoV-2 coronavirus laboratory and point-of-care diagnostics. Diagnostics 10(6), 434 (2020)

    Article  Google Scholar 

  63. Adams, E.R., Ainsworth, M., Anand, R., Andersson, M.I., Auckland, K., Baillie, J.K., Barnes, E., Beer, S., Bell, J.I., Berry, T., Bibi, S.: Antibody testing for COVID-19: a report from the National COVID Scientific Advisory Panel. Wellcome Open Res. 5(139), 139 (2020)

    Article  Google Scholar 

  64. Deeks, J.J., Raffle, A.E.: Lateral flow tests cannot rule out SARS-CoV-2 infection. BMJ 371, m4787 (2020)

    Article  Google Scholar 

  65. Sherlock Biosciences receives FDA emergency use authorization for CRISPR SARS-CoV-2 rapid diagnostic. Sherlock Biosciences Sherlock.bio (2021). https://sherlock.bio/sherlock-biosciences-receives-fdaemergency-use-authorization-for-crispr-sars-cov-2-rapid-diagnostic/. Accessed 31 Jan 2021

  66. Patchsung, M., Jantarug, K., Pattama, A., Aphicho, K., Suraritdechachai, S., Meesawat, P., Sappakhaw, K., Leelahakorn, N., Ruenkam, T., Wongsatit, T., Athipanyasilp, N.: Clinical validation of a Cas13-based assay for the detection of SARS-CoV-2 RNA. Nat. Biomed. Eng. 4(12), 1140–9 (2020)

    Article  Google Scholar 

  67. Joung, J., Ladha, A., Saito, M., Segel, M., Bruneau, R., Huang, M.L., Kim, N.G., Yu, X., Li, J., Walker, B.D., Greninger, A.L.: Point-of-care testing for COVID-19 using SHERLOCK diagnostics. MedRxiv (2020)

    Google Scholar 

  68. Ding, X., Yin, K., Li, Z., Lalla, R.V., Ballesteros, E., Sfeir, M.M., Liu, C.: Ultrasensitive and visual detection of SARS-CoV-2 using all-in-one dual CRISPR-Cas12a assay. Nat. Commun. 11(1), 1 (2020)

    Article  Google Scholar 

  69. Shirato, K., Semba, S., El-Kafrawy, S.A., Hassan, A.M., Tolah, A.M., Takayama, I., Kageyama, T., Notomi, T., Kamitani, W., Matsuyama, S., Azhar, E.I.: Development of fluorescent reverse transcription loop-mediated isothermal amplification (RT-LAMP) using quenching probes for the detection of the Middle East respiratory syndrome coronavirus. J. Virol. Methods 1(258), 41–8 (2018)

    Article  Google Scholar 

  70. Oxford researchers develop portable COVID-19 test costing less than $25. Fierce-Biotech (2021). https://www.fiercebiotech.com/medtech/oxford-researchers-develop-portable-covid-19-test-costing-less-than25. Accessed 31 Jan 2021

  71. Cepheid charging four times more than it should for coronavirus COVID-19 tests | MSF. Médecins Sans Frontières (MSF) International (2021). https://www.msf.org/diagnostic-company-cepheid-charging-more-itshould-covid-19-tests. Accessed 31 Jan 2021

  72. Fda.gov (2021). https://www.fda.gov/media/138826/download. Accessed 31 Jan 2021

  73. Scan, P.: Preparing for a CT Scan, WakeMed Health & Hospitals, Raleigh & Wake County, NC. Wakemed.org (2021). https://www.wakemed.org/care-and-services/imaging-services/ct-scan/preparing-for-a-ctscan. Accessed 31 Jan 2021

  74. Gooch, K.: The out-of-pocket costs of X-rays, CT scans across 3 states: 4 things to know. Beckershospitalreview.com (2021). https://www.beckershospitalreview.com/finance/the-out-of-pocket-costs-of-x-rays-ctscans-across-3-states-4-things-to-know.html#:~:text=Researchers%20found%20that%20across%20hospitals,3. Accessed 31 Jan 2021

  75. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., Xia, L.: Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2), E32–E40 (2020)

    Article  Google Scholar 

  76. Panwar, H., Gupta, P.K., Siddiqui, M.K., Morales-Menendez, R., Singh, V.: Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos Solitons Fractals 138, 109944 (2020)

    Article  MathSciNet  Google Scholar 

  77. Ijitee.org (2021). https://www.ijitee.org/wp-content/uploads/Souvenir_Volume-9_Issue-6_April_2020.pdf. Accessed 31 Jan 2021

  78. Ahmad, F., Farooq, A., Ghani, M.U.: Deep Ensemble model for classification of novel coronavirus in chest X-ray images. Comput. Intell. Neurosci. 12, 2021 (2021)

    Google Scholar 

  79. Ji, T., Liu, Z., Wang, G., Guo, X., Lai, C., Chen, H., Huang, S., Xia, S., Chen, B., Jia, H., Chen, Y.: Detection of COVID-19: A review of the current literature and future perspectives. Biosens. Bioelectron. 166, 112455 (2020)

    Article  Google Scholar 

  80. Wong, M.L., Medrano, J.F.: Real-time PCR for mRNA quantitation. Biotechniques 39(1), 75–85 (2005)

    Article  Google Scholar 

  81. Long, C., Xu, H., Shen, Q., Zhang, X., Fan, B., Wang, C., Zeng, B., Li, Z., Li, X., Li, H.: Diagnosis of the coronavirus disease (COVID-19): rRT-PCR or CT? Eur. J. Radiol. 126, 108961 (2020)

    Article  Google Scholar 

  82. Li, Y., Yao, L., Li, J., Chen, L., Song, Y., Cai, Z., Yang, C.: Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19. J. Med. Virol. 92(7), 903–908 (2020)

    Article  Google Scholar 

  83. Suo, T., Liu, X., Feng, J., Guo, M., Hu, W., Guo, D., Ullah, H., Yang, Y., Zhang, Q., Wang, X., Sajid, M.: ddPCR: a more accurate tool for SARS-CoV-2 detection in low viral load specimens. Emerg. Microbes Infect. 9(1), 1259–1268 (2020)

    Article  Google Scholar 

  84. Singh, R.S., Singh, T., Pandey, A.: Microbial enzymes—an overview. Adv. Enzyme Technol. 1, 1–40 (2019)

    Google Scholar 

  85. Gurbuz, M.: Molecular and serological tests for COVID-19. Eurasian J. Pulmonol. 22(4), 29 (2020)

    Article  Google Scholar 

  86. Peeling, R.W., Wedderburn, C.J., Garcia, P.J., Boeras, D., Fongwen, N., Nkengasong, J., Sall, A., Tanuri, A., Heymann, D.L.: Serology testing in the COVID-19 pandemic response. Lancet Infect. Dis. 20 (2020)

    Google Scholar 

  87. COVID WT, Montrose TI: A pandemic response update for the Board of Health

    Google Scholar 

  88. Chertow, D.S.: Next-generation diagnostics with CRISPR. Science 360(6387), 381–382 (2018)

    Article  Google Scholar 

  89. Wise, J.: COVID-19: safety of lateral flow tests questioned after they are found to miss half of cases. BMJ 371, m4744 (2020)

    Article  Google Scholar 

  90. Xiang, X., Qian, K., Zhang, Z., Lin, F., Xie, Y., Liu, Y., Yang, Z.: CRISPR-Cas systems based molecular diagnostic tool for infectious diseases and emerging 2019 novel coronavirus (COVID-19) pneumonia. J. Drug Target. 28(7–8), 727–731 (2020)

    Article  Google Scholar 

  91. Wang, D., Hu, B., Hu, C., Zhu, F., Liu, X., Zhang, J., Wang, B., Xiang, H., Cheng, Z., Xiong, Y., et al.: Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus– infected pneumonia in Wuhan, China. JAMA 323(11), 1061–1069 (2020)

    Article  Google Scholar 

  92. Jin, Y.-H., Cai, L., Cheng, Z.-S., Cheng, H., Deng, T., Fan, Y.-P., Fang, C., Huang, D., Huang, L.-Q., Huang, Q., et al.: A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil. Med. Res. 7(1), 4 (2020)

    Google Scholar 

  93. Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., Ji, W.: Sensitivity of chest CT for covid-19: comparison to RT-PCR. Radiology 296(2), E115–E117 (2020)

    Article  Google Scholar 

  94. 2021. https://www.fda.gov/radiationemitting-products/medical-imaging/medical-x-ray-imag-ing#:~:text=CT%2C%20radiography%2C%20and%20fluoroscopy%20all,computer%20screen)%20for%20recording%20or. Accessed 31 Jan 2021

  95. Sakib, S., Siddique, M.A., Khan, M.M., Yasmin, N., Aziz, A., Chowdhury, M., Tasawar, I.K.: Detection of COVID-19 disease from chest X-ray images: a deep transfer learning framework. medRxiv (2020)

    Google Scholar 

  96. Jain, R., Gupta, M., Taneja, S., Hemanth, D.J.: Deep learning based detection and analysis of COVID-19 on chest X-ray images. Appl. Intell. 51, 1690–1700 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zejneba Jassin .

Editor information

Editors and Affiliations

Ethics declarations

The authors have nothing to declare.

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jassin, Z., Heric, A., Mujkic, A., Baralic, E. (2021). COVID-19 Diagnostic Approaches: An Overview. In: Badnjevic, A., Gurbeta Pokvić, L. (eds) CMBEBIH 2021. CMBEBIH 2021. IFMBE Proceedings, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-73909-6_100

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-73909-6_100

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73908-9

  • Online ISBN: 978-3-030-73909-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics