AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data

K C Santosh, K C Santosh

Abstract

The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible global threats. Beside clinical procedures and treatments, since Artificial Intelligence (AI) promises a new paradigm for healthcare, several different AI tools that are built upon Machine Learning (ML) algorithms are employed for analyzing data and decision-making processes. This means that AI-driven tools help identify COVID-19 outbreaks as well as forecast their nature of spread across the globe. However, unlike other healthcare issues, for COVID-19, to detect COVID-19, AI-driven tools are expected to have active learning-based cross-population train/test models that employs multitudinal and multimodal data, which is the primary purpose of the paper.

Keywords: Active learning; Artificial intelligence; COVID-19; Cross-population train/test models; Machine learning; Multitudinal and multimodal data.

Conflict of interest statement

Author declared no conflict of interest.

Figures

Fig. 1
Fig. 1
Known locations of coronavirus cases by county in the US. Circles are sized by the number of people there who have tested positive, which may differ from where they contracted the illness. More than 100 cases have been identified in New York. (source: https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html, March 09, 2020)
Fig. 2
Fig. 2
Countries, territories or areas with reported confirmed cases of COVID- 19 (source: https://www.who.int/docs/default-source/coronaviruse/situation-reports/ 20,200,309-sitrep-49-covid-19.pdf?sfvrsn = 70dabe61_4, March 09, 2020)
Fig. 3
Fig. 3
For time-series data, a schema of Active Learning (AL) model is provided. For better understanding, AL (in dotted red circle) is used with Deep Learning (DL) for all possible data types. In AL, expert’s feedback is used in parallel with the decisions from each data type. Since DL are data dependent, separate DLs are used for different data type. The final decision is made based on multitudinal and multimodal data
Fig. 4
Fig. 4
Chest X-ray: Bilateral focal consolidation, lobar consolidation, and patchy consolidation are clearly observed (check lower lung [1])
Fig. 5
Fig. 5
Chest CT: An axial CT image shows ground-glass opacities with a rounded morphology (arrows) in the right middle and lower lobes [21]

References

    1. Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, Hu Y, Tao ZW, Tian JH, Pei YY, Yuan ML, Zhang YL, Dai FH, Liu Y, Wang QM, Zheng JJ, Xu L, Holmes EC, Zhang YZ. A new coronavirus associated with human respiratory disease in china. Nature. 2020;44(59):265–269. doi: 10.1038/s41586-020-2008-3.
    1. Centers for Disease Control and Prevention, Morbidity and Mortality Weekly Report (MMWR), Public Health Response to the Coronavirus Disease 2019 Outbreak – United States, February 24, 2020.
    1. Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., Xia, J., Yu, T., Zhang, X., and Zhang, L., Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in wuhan, china: A descriptive study. The Lancet 395(10223):507–513, 2020. .
    1. WHO Report, Coronavirus disease 2019 (COVID-19) Situation Report – 49 (2020 (accessed March 09, 2020)), .
    1. Wu, Z., and McGoogan, J.M., Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72,314 cases from the Chinese center for disease control and prevention. JAMA, 2020. 10.1001/jama.2020.2648.
    1. Medscape Medical News, The WHO declares public health emergency for novel coronavirus (January 30, 2020). .
    1. Long, J.B., and Ehrenfeld, J.M., The role of augmented intelligence (ai) in detecting and preventing the spread of novel coronavirus. Journal of Medical Systems 44(59), 2020. 10.1007/s10916-020-1536-6.
    1. Paules, C. I., Marston, H. D., and Fauci, A. S., Coronavirus infections – more than just the common cold. JAMA 323(8):707–708, 2020. 10.1001/jama.2020.0757.
    1. Drosten C, Gunther S, et al. Identification of a novel corona virus in patients with severe acute respiratory syndrome. N Engl J Med. 2003;348:1967–1976. doi: 10.1056/NEJMoa030747.
    1. Peiris, J., Lai, S., Poon, L., Guan, Y., Yam, L., Lim, W., Nicholls, J., Yee, W., Yan, W., Cheung, M., Cheng, V., Chan, K., Tsang, D., Yung, R., Ng, T., and Yuen, K., Coronavirus as a possible cause of severe acute respiratory syndrome. The Lancet 361(9366), 2003. 10.1016/S0140-6736(03)13077-2.
    1. Song, Z., Xu, Y., Bao, L., Zhang, L., Yu, P., Qu, Y., Zhu, H., Zhao, W., Han, Y., and Qin, C., From sars to mers, thrusting coronaviruses into the spotlight. Viruses 11(1), 2019.
    1. de Wit E, van Doremalen N, Falzarano D, Munster VJ. Sarsandmers: Recentinsights into emerging coronaviruses. Nature Reviews Microbiology. 2016;14(8):523–534. doi: 10.1038/nrmicro.2016.81.
    1. Sammut, C., and Webb, G.I. (eds.), Encyclopedia of machine learning and data mining. Springer, 2017. 10.1007/978-1-4899-7687-1.
    1. Chen, J., Wu, L., Zhang, J., Zhang, L., Gong, D., Zhao, Y., Hu, S., Wang, Y., Hu, X., Zheng, B., Zhang, K., Wu, H., Dong, Z., Xu, Y., Zhu, Y., Chen, X., Yu, L., and Yu, H., Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study. medRxiv, 2020. .
    1. Guo, Q., Li, M., Wang, C., Wang, P., Fang, Z., Tan, J., Wu, S., Xiao, Y., and Zhu, H., Host and infectivity prediction of wuhan 2019 novel coronavirus using deep learning algo- rithm. bioRxiv (2020), .
    1. Dewey M, Schlattmann P. Deep learning and medical diagnosis. The Lancet. 2019;394:1710–1711. doi: 10.1016/S0140-6736(19)32498-5.
    1. The Wall Street Journal, Coronavirus reveals limits of AI health tools (2020 (accessed February 29, 2020)), .
    1. Bouguelia M, Nowaczyk S, Santosh KC, Verikas A. Agreeing to disagree: Active learning with noisy labels without crowdsourcing. Int. J. Machine Learning & Cybernetics. 2018;9(8):1307–1319. doi: 10.1007/s13042-017-0645-0.
    1. Technology Org, AI algorithm detects coronavirus infections in patients from CT scans with 96% accuracy (2020 (accessed March 02, 2020)),
    1. Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., Tao, Q., Sun, Z., and Xia, L., Correlation of chest ct and rt-pcr testing in coronavirus disease 2019 (covid-19) in china: A report of 1014 cases. Radiology 0(0):200642, 2020. 10.1148/radiol.2020200642.
    1. Bernheim, A., Mei, X., Huang, M., Yang, Y., Fayad, Z.A., Zhang, N., Diao, K., Lin, B., Zhu, X., Li, K., Li, S., Shan, H., Jacobi, A., and Chung, M., Chest ct findings in coronavirus disease-19 (covid-19): Relationship to duration of infection. Radiology 0(0):200463, 2020. 10.1148/radiol.2020200463.
    1. Fong SJ, Li G, Dey N, Crespo RG, Herrera-Viedma E. Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak. International Journal of Interactive Multimedia and Artificial Intelligence. 2020;6:51–61. doi: 10.9781/ijimai.2020.02.005.

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe