bebi 21(2): e6

Research Article

Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients

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  • @ARTICLE{10.4108/eai.12-3-2021.169028,
        author={Anđela Blagojević and Tijana Šušteršič and Ivan Lorencin and Sandi Baressi Šegota and Dragan Milovanović and Danijela Baskić and Dejan Baskić and Zlatan Car and Nenad Filipović},
        title={Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients},
        journal={EAI Endorsed Transactions on Bioengineering and Bioinformatics},
        volume={1},
        number={2},
        publisher={EAI},
        journal_a={BEBI},
        year={2021},
        month={3},
        keywords={COVID-19, machine learning, personalized model, U-net, classification, predictive models, finite element simulation},
        doi={10.4108/eai.12-3-2021.169028}
    }
    
  • Anđela Blagojević
    Tijana Šušteršič
    Ivan Lorencin
    Sandi Baressi Šegota
    Dragan Milovanović
    Danijela Baskić
    Dejan Baskić
    Zlatan Car
    Nenad Filipović
    Year: 2021
    Combined machine learning and finite element simulation approach towards personalized model for prognosis of COVID-19 disease development in patients
    BEBI
    EAI
    DOI: 10.4108/eai.12-3-2021.169028
Anđela Blagojević1,2, Tijana Šušteršič1,2, Ivan Lorencin3, Sandi Baressi Šegota3, Dragan Milovanović4,5, Danijela Baskić4, Dejan Baskić5,6, Zlatan Car3, Nenad Filipović1,2,*
  • 1: University of Kragujevac, Faculty of Engineering, Sestre Janjić 6, 34000 Kragujevac, Serbia
  • 2: Bioengineering Research and Development Center (BioIRC), Prvoslava Stojanovića 6, 34000 Kragujevac, Serbia
  • 3: University of Rijeka, Faculty of Engineering, Vukovarska 58, 51000 Rijeka, Croatia
  • 4: Clinical Centre Kragujevac, Zmaj Jovina 30, 34000 Kragujevac, Serbia
  • 5: University of Kragujevac, Faculty of Medical Sciences, Svetozara Markovića 69, 34000 Kragujevac, Serbia
  • 6: Institute of Public Health Kragujevac, Nikole Pašića 1, 34000 Kragujevac, Serbia
*Contact email: fica@kg.ac.rs

Abstract

INTRODUCTION: Machine learning algorithms and in silico models for the COVID-19 have been used to classify infectious people and predict their condition in time.

OBJECTIVES: This study aims at creating a personalized model that combines machine learning and finite element simulation approach in order to predict development of COVID-19 infection in patients.

METHODS: The methodology combines several aspects (1) classification of patients into several classes of clinical condition (2) segmentation of human lungs in X ray images (3) finite element simulation to investigate the spreading of SARS-COV-2 virion in the lungs.

RESULTS: The findings show accuracy larger than 90% in all aspects of methodology. FE simulation has revealed that the distribution of airflow in the lung changes in time with the infection.

CONCLUSION: The key benefit of our proposed method is that it combines several methods that will be further improved in order to create a truly unique combined methodology for predictive models in patients infected with COVID-19.