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

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 r...

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Bibliographic Details
Published in:Mathematics (Basel) 2023-03, Vol.11 (6), p.1279
Main Authors: Jin, Suya, Liu, Guiyan, Bai, Qifeng
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Artificial neural networks
Back propagation
Biomedical data
Coronaviruses
COVID-19
Deep learning
Diagnosis
Disease transmission
Epidemiology
Health services
Laboratory tests
Machine learning
Mathematics
Medical imaging
multi-modal learning
Neural networks
Pandemics
Pneumonia
Prognosis
Radiation
Severe acute respiratory syndrome coronavirus 2
Viral infections
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Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11061279