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Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection

Author

Listed:
  • Suya Jin

    (Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Guiyan Liu

    (Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China
    These authors contributed equally to this work.)

  • Qifeng Bai

    (Key Lab of Preclinical Study for New Drugs of Gansu Province, Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, China)

Abstract

Deep learning is a sub-discipline of artificial intelligence that uses artificial neural networks, a machine learning technique, to extract patterns and make predictions from large datasets. In recent years, it has achieved rapid development and is widely used in numerous disciplines with fruitful results. Learning valuable information from complex, high-dimensional, and heterogeneous biomedical data is a key challenge in transforming healthcare. In this review, we provide an overview of emerging deep-learning techniques, COVID-19 research involving deep learning, and concrete examples of deep-learning methods in COVID-19 diagnosis, prognosis, and treatment management. Deep learning can process medical imaging data, laboratory test results, and other relevant data to diagnose diseases and judge disease progression and prognosis, and even recommend treatment plans and drug-use strategies to accelerate drug development and improve drug quality. Furthermore, it can help governments develop proper prevention and control measures. We also assess the current limitations and challenges of deep learning in therapy precision for COVID-19, including the lack of phenotypically abundant data and the need for more interpretable deep-learning models. Finally, we discuss how current barriers can be overcome to enable future clinical applications of deep learning.

Suggested Citation

  • Suya Jin & Guiyan Liu & Qifeng Bai, 2023. "Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1279-:d:1089713
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    References listed on IDEAS

    as
    1. Farrukh Saleem & Abdullah Saad AL-Malaise AL-Ghamdi & Madini O. Alassafi & Saad Abdulla AlGhamdi, 2022. "Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review," IJERPH, MDPI, vol. 19(9), pages 1-18, April.
    2. Mojtaba Nabipour & Pooyan Nayyeri & Hamed Jabani & Amir Mosavi, 2020. "Deep learning for Stock Market Prediction," Papers 2004.01497, arXiv.org.
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