A System for Enhancing Human-level Performance in COVID-19 Antibody Detection

  • Victor Henrique Alves Ribeiro Hilab
  • Gabriela Steinhaus Hilab
  • Evair Borges Severo Hilab
  • José Raniery Ferreira Junior Hilab
  • Luiz José Lucas Barbosa Hilab
  • Marcelo Cossetin Hilab
  • Marcus Vinícius Mazega Figueiredo Hilab


The world currently suffers from the global COVID-19 pandemic. Billions of people have been impacted, and millions of casualties have already occurred. Therefore, it is of extreme importance to identify individuals contaminated by SARS-CoV-2, allowing governments to plan actions to reduce further impacts. In this context, this work employed machine learning to improve the detection of SARS-CoV-2 antibodies in blood exams. Models have been developed in a real-world scenario with 500 thousand exams and were deployed in a remote laboratory for experiments. Results indicate that the models averaged sensitivity and specificity of 95%, and thus, they could aid COVID-19 antibody detection and the decision-making process of biomedical specialists.


Beeching, N. J., Fletcher, T. E., and Beadsworth, M. B. J. (2020). Covid-19: testing times. BMJ, 369.

Ferreira, J. R. and Cardenas, D. A. C. (2021). The potential role of radiogenomics in precision medicine for COVID-19. Journal of Thoracic Imaging. DOI:10.1097/RTI.0000000000000586.

Ferreira Junior, J. R., Cardenas, D. A. C., Moreno, R. A., Rebelo, M. F. S., Krieger, J. E., and Gutierrez, M. A. (2021). Novel chest radiographic biomarkers for COVID-19 using radiomic features associated with diagnostics and outcomes. Journal of Digital Imaging. DOI:10.1007/s10278-021-00421-w.

Friedman, J., Hastie, T., Tibshirani, R., et al. (2001). The elements of statistical learning, volume 1. Springer series in statistics New York.

Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y. (2016). Deep learning, volume 1. MIT press Cambridge.

He, H. and Ma, Y. (2013). Imbalanced learning: foundations, algorithms, and applications. John Wiley & Sons.

Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223):497–506.

ISO 22870:2016(en) (2016). Point-of-care testing (POCT) — Requirements for quality and competence. Standard, International Organization for Standardization, Geneva.

Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.

Yuan, M., Yin,W., Tao, Z., Tan,W., and Hu, Y. (2020). Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus inWuhan, China. PloS ONE, 15(3):e0230548.

Zhao, J., Yuan, Q., Wang, H., Liu, W., Liao, X., Su, Y., Wang, X., Yuan, J., Li, T., Li, J., Qian, S., Hong, C., Wang, F., Liu, Y., Wang, Z., He, Q., Li, Z., He, B., Zhang, T., Fu, Y., Ge, S., Liu, L., Zhang, J., Xia, N., and Zhang, Z. (2020). Antibody Responses to SARS-CoV-2 in PatientsWith Novel Coronavirus Disease 2019. Clinical Infectious Diseases, 71(16):2027–2034.
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RIBEIRO, Victor Henrique Alves; STEINHAUS, Gabriela; SEVERO, Evair Borges; FERREIRA JUNIOR, José Raniery; BARBOSA, Luiz José Lucas; COSSETIN, Marcelo; FIGUEIREDO, Marcus Vinícius Mazega. A System for Enhancing Human-level Performance in COVID-19 Antibody Detection. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 21. , 2021, Evento Online. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2021 . p. 224-233. ISSN 2763-8952. DOI: https://doi.org/10.5753/sbcas.2021.16067.