Skip to main content

Application of AI in Diagnosing and Drug Repurposing in COVID 19

  • Conference paper
  • First Online:
Advances in Artificial Intelligence, Software and Systems Engineering (AHFE 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 271))

Included in the following conference series:

  • 1848 Accesses

Abstract

Corona virus is a type of virus. We can find diverse kinds of Corona viruses among them only few of them cause disease and when they cause disease, it would be cold and other mild respiratory illness. However, couple of corona viruses causes severe diseases like (MERS) Middle East respiratory syndrome and (SERS) Severe Acute Respiratory Syndrome. Scientists identified this virus as the cause of a disease outbreak in Wuhan, China in December 2019. This virus is identified as (SARS-CoV-2) Severe Acute Respiratory Syndrome Corona Virus 2. The disease is well known as COVID-19. Corona virus is declared as an outbreak pandemic on 11 March 2020 by World Health Organization (WHO). Via biomedical exploration, clinical science, precision medicine and medical diagnostics/devices, Artificial Intelligence (AI) is quickly becoming an important approach. These tools will discover a new ways for researchers, clinicians, and patient, helping to make choices that are educated and to produce better results. These methods have the potential to increase the efficacy and efficiency of health research and treatment ecosystem when applied in healthcare environments, and potentially improve the quality of patient care. Today in this world of AI and network medicine which will give us application of information science. In this work we have discussed about how we are utilizing this new trend of AI in drug repurposing during this pandemic situation.

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. Vaishya, R., Javaid, M., Haleem Khan, I., Haleem, A.: Artificial intelligence (AI) applications for COVID-19 pandemic. Diabet. Metabol. Syndr. Clin. Res. Rev. 14 (2020). https://doi.org/10.1016/j.dsx.2020.04.012

  2. https://ourworldindata.org

  3. Zhou, Y., Wang, F., Tang, J., Nussinov, R., Cheng, F.: Artificial intelligence in COVID-19 drug repurposing. Lancet Digit. Health (2020). https://doi.org/10.1016/S2589-7500(20)30192-8.

  4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  5. Li, L., et al.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 200905 (2020)

    Google Scholar 

  6. Xu, X., et al.: Deep learning system to screen coronavirus disease 2019 pneumonia. arXiv preprint arXiv:2002.09334 (2020)

  7. Ghoshal, B., Tucker, A.: Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv preprint arXiv:2003.10769 (2020)

  8. Wang, S., et al.: A deep learning algorithm using CT images to screen for corona virus disease (COVID-19). medRxiv (2020). https://doi.org/10.1101/2020.02.14.20023028

  9. Bai, X., et al.: Predicting COVID-19 malignant progression with AI techniques. medRxiv (2020). https://doi.org/10.1101/2020.03.20.20037325

  10. Jin, C., et al.: Development and evaluation of an AI system for COVID-19. medRxiv (2020). https://doi.org/10.1101/2020.03.20.20039834

  11. Jin, S., et al.: AI-assisted CT imaging analysis for COVID-19 screening: building and deploying a medical AI system in four weeks. medRxiv (2020). https://doi.org/10.1101/2020.03.19.20039354

  12. Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks. arXiv preprint arXiv:2003.10849 (2020)

  13. Wang, L., Wong, A.: COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. arXiv preprint arXiv:2003.09871 (2020)

  14. Gozes, O., et al.: Rapid AI development cycle for the coronavirus (COVID-19) pandemic: initial results for automated detection and patient monitoring using deep learning CT image analysis. arXiv preprint arXiv:2003.05037 (2020)

  15. Chowdhury, M.E., et al.: Can AI help in screening viral and COVID-19 pneumonia? arXiv preprint arXiv:2003.13145 (2020)

  16. Maghdid, H.S., Asaad, A.T., Ghafoor, K.Z., Sadiq, A.S., Khan, M.K.: Diagnosing COVID-19 pneumonia from X-ray and CT images using deep learning and transfer learning algorithms. arXiv preprint arXiv:2004.00038 (2020)

  17. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  18. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

    Google Scholar 

  19. Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv preprint arXiv:1602.07360 (2016)

  20. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to G. K. Ravikumar .

Editor information

Editors and Affiliations

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

Ravikumar, G.K., Bharadwaj, S., Niveditha, N.M., Narendra, B.K. (2021). Application of AI in Diagnosing and Drug Repurposing in COVID 19. In: Ahram, T.Z., Karwowski, W., Kalra, J. (eds) Advances in Artificial Intelligence, Software and Systems Engineering. AHFE 2021. Lecture Notes in Networks and Systems, vol 271. Springer, Cham. https://doi.org/10.1007/978-3-030-80624-8_15

Download citation

Publish with us

Policies and ethics