Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

Abstract

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19's spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long /Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications.

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

“…There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning [ 3 ]. As a consequence of the emergent interest in deep learning, a number of techniques have been developed within this field with respect to the diagnosis, treatment and prognosis of the COVID-19 disease, including densely connected neural networks, recurrent networks and generative adversarial networks [ 4 ]. There is currently no consensus as to which of these techniques yields the most robust prognostic models [ 5 ], and whilst several models have been developed at a time when they are urgently required, there are a number of limitations which have impeded their use [ 6 ].…”

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes

“…3 As a consequence of the emergent interest in deep learning, a number of techniques have been developed within this eld with respect to the diagnosis, treatment and prognosis of the COVID-19 disease, including densely connected neural networks, recurrent networks and generative adversarial networks. 4 There is currently no consensus as to which of these techniques yields the most robust prognostic models, 5 and whilst several models have been developed at a time when they are urgently required, there are a number of limitations which have impeded their use. 6 Several of the current models have been found to be highly susceptible to bias.…”

Applying Deep Learning for Genome Detection of Coronavirus

“…The researchers in [7] focus on analyzing the data available at web-based platforms to demonstrate the trends about the effect of 'SARS-CoV-2' across the globe. The authors in [8] reviewed the findings of the recent literature published on 'COVID-19'. They highlighted the challenges in its diagnosis and treatment.…”