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.


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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.