Deep Learning in COVID-19: History
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Contributor:
Suya Jin
,
Guiyan Liu
,
Qifeng Bai

Various deep-learning (DL) methods that utilize a combination of omics data and imaging data have been applied to the diagnosis, prognosis, and treatment options of clinical COVID-19 patients. Even with the emerging deep-learning methods, human intervention is still essential in the clinical diagnosis and treatment of COVID-19 patients.

  • artificial intelligence
  • deep learning
  • multi-modal learning

1. Deep Learning for Diagnosis of COVID-19

Various deep-learning (DL) methods that utilize a combination of omics data and imaging data have been applied to the diagnosis, prognosis, and treatment options of clinical COVID-19 patients. However, even with the emerging deep-learning methods, human intervention is still essential in the clinical diagnosis and treatment of COVID-19 patients. Therefore, the goal of DL is not to surpass or replace humans, but rather to provide decision-support tools to help researchers studying COVID-19 and health professionals in the clinical management of COVID-19 patients.

COVID-19 is traditionally diagnosed by real-time RT-PCR testing of respiratory or blood samples [1] (Figure 1). However, considering the simplicity and sensitivity of the test, CXR radiography has become the mainstay of screening, triaging, and diagnosing varieties of COVID-19. Researchers [2][3] noted that the majority of the COVID-19 positive cases in their study presented bilateral radiographic abnormalities in CXR images, such as ground-glass opacity, bilateral abnormalities, and interstitial abnormalities in CXR and CT images. Indeed, early works on COVID-19 imagery identified the existence of pulmonary lesions in non-severe and even recovered patients [4]. Among various deep-learning classifiers, CNNs, in particular, have been enormously effective in computer vision and medical image analysis tasks. COVID-Net represents one of the earliest convolutional networks designed for detecting COVID-19 cases automatically from CXR images [5]. This architecture design consists of two parts: a human–computer collaborative design approach and a machine-driven design exploration part. A lightweight residual projection–expansion–projection–extension (PEPX) design pattern is used in this architecture. An interpretability-driven check was also performed for decision validation, achieving 87.1 percent recall and 93.3 percent accuracy. To reduce the amount of data and time required for training, transfer learning may be an appropriate solution [6]. Researchers have achieved state-of-the-art performance on pneumonia recognition using ensemble models with pre-trained architectures trained on ImageNet, using AlexNet, DenseNet121, Inception V3, GoogLeNet and ResNet18 to achieve 96.4 percent accuracy and 99.62 percent recall on unseen data from the Guangzhou Women and Children’s Medical Center dataset [7]. COVID CAPS [8], a CNN model based on CapsNets, was able to process small data sets with 95.7 percent accuracy. Most computer vision tasks, such as image classification, semantic segmentation, object recognition, etc., are based on 2D CNN [9]. Since high-dimensional data is difficult for humans to understand, the application of multidimensional CNN in 3D is not common, as described above. Hybrid-COVID [10] is a novel Hybrid 2D/3D framework based on CNN that uses the potential synergy between a pre-trained VGG16 model (i.e., 2D CNN) and a shallow 3D CNN to efficiently and effectively diagnose COVID-19 from CXR images with 96.91 percent accuracy. Researchers proposed a medical image segmentation method for COVID-19 lung CT image segmentation using an advanced high-density GAN dataset combined with a multi-layer attention mechanism approach from U-Net [11].

In addition to CXRs, researchers can use several clinical approaches to diagnose COVID-19. Researchers proposed an explainable classification model to automatically differentiate COVID-19 and community-acquired pneumonia (CAP) from healthy lungs in radiographic images [12]. Lung Ultrasound (LUS) imaging can also be used as an alternative to CXRs and CT to identify lung disease [13]
 

Figure 1. Schematic of COVID-19. It shows the symptoms, diagnosis, and management of COVID-19.

2. Deep Learning for COVID-19 Early Warning System

In addition to the diagnosis of COVID-19, it is also important to predict the malignant progression of COVID-19 [14][15][16][17][18]. The sudden progression to critical illness in patients with COVID-19 is a major concern [19][20], and early identification of the malignant progression of COVID-19 can reduce the heterogeneity of patient stratification, optimize diagnosis and treatment, improve the efficiency of medical resource allocation as well as the response capacity of medical systems to emergencies, and, ultimately, reduce mortality [21]. A study has shown that a deep-learning-based survival model [22] can predict the risk of critical illness in COVID-19 patients based on clinical features at admission. By creating an online computational tool to classify patients on admission, the model in turn identifies patients at high risk of serious illness, ensuring that patients most at risk of serious illness receive early access to appropriate care and allowing for the efficient allocation of health resources.
The Cox proportional risk model (CPH) is a widely used statistical model which is a multivariate linear regression model that depends on regression analysis to confirm the association between predictive covariates (such as clinical features) and event occurrence risks (such as “death”). For example, researchers used least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis to identify important factors associated with COVID-19-related hospitalization rates [23]. Hypertension, higher neutrophil-to-lymphocyte ratios, and N-terminal pro-b-type natriuretic peptide (NT-proBNP) values were identified and used to develop prognostic line maps. Linear plots show good discrimination and capability to predict the 14-day and 21-day survival probability of COVID-19 patients and evaluation by C-index, AUC values, and calorimetric plots indicate good performance and high value for clinical use. In biomedical images, COVID-MANet uses a semantic segmentation method to categorize CT images of COVID-19-positive specimens into mild, moderate, severe, and critical based on the proportion of infected disease pixels, with mapping of the activation Grad-CAM used to interpret the diagnosis and generate a map survey model of location for each type of disease, except for the interpretation of the map survey model for COVID-19 infection. This increased understanding of risk factors, and the resulting risk stratification, can help clinicians develop medical guidelines to improve the management of risk-stratified care for COVID-19 patients. In the actual clinical experience, mild cases of COVID-19 are usually self-limited, but severe cases require more medical attention. Online submission of clinical information and medical personnel can be used to predict the risk index for admission of patients with shunt disease, as well as the treatment of patients with corresponding scheduling plans, ensuring that patients receive prompt follow-up treatment as early as possible so that medical resources can be used effectively.

3. Deep Learning for COVID-19 Prediction

In the context of the COVID-19 pandemic, it is important to accurately predict the development of the epidemic [24] and to identify and short-term estimate the final size and peak time of the epidemic as early as possible [25] (Figure 1). Early prediction [26][27] using mathematical and statistical models combined with available data can be effective in helping governments develop appropriate prevention [28] and control measures [29]. In the early days of the outbreak, many mathematical models were used to predict the COVID-19 pandemic, such as crowd flow models that reduced infection rates among people through enforcement measures, phenomenological models for short-term prediction of COVID-19, a dynamical model (a developed generalized susceptible-exposed-infected-removed (SEIR) model), a susceptible-infectious-recovered-dead (SIRD) model, gated recurrent units (GRUs), long short-term memory (LSTM), an autoregressive integrated moving average (ARIMA) model, simple RNN, bidirectional LSTM (BiLSTM), variational autoencoder (VAE), a neural network model, and an ensemble model using four machine learning methods, namely, support vector machine, logistic regression, gradient boosted decision tree, and neural network.
Kafieh et al. [30] described a kind of prediction method based on deep learning that could help medical and government agencies in pandemic preparation and adjustment. Three tests were used in the study of this model for predicting COVID-19 disease, including basic information, COVID-19 data, and detailed information for each country. In the study, the relevant information was first extracted and processed from the data sources, and then the COVID-19 data from each country was used to train these different machine learning models. Results of five promising models are reported in experiments using different machine learning models, including random forest (RF), LSTM with regular features (LSTM-R), MLP, multivariate LSTM (M-LSTM), and LSTM with extended features (LSTM-E). After evaluating five models, the results show that M-LSTM is the best network model to identify the true size of the pandemic. This model expects that the prediction in each case is consistent with the previous action, and that any new action will lead to different results. Therefore, if the action has a positive impact, the predicted number of infections will decrease. Negative behavior, on the other hand, leads to an increase in the number of predictions. The underlying assumption of the model is the stability of environmental measurements; however, since researchers live in an uncontrollable situation, each decision changes the trajectory of the epidemic. Therefore, the goal of this model is to determine the intensity and timing of the peak, the expected total number of cases during the COVID-19 pandemic, and the impact of government policies on the number of infections. By identifying these outcomes, the allocation of resources for primary prevention, secondary prevention, risk communication, and preparedness planning can be improved.

4. Deep Learning for Novel Coronavirus Molecules

SARS-CoV-2 is the main cause of the COVID-19 pandemic [31][32]. During the course of the ongoing epidemic, the virus has mutated many times. Structural differences between different SARS-CoV-2 strains may also be responsible for their different infectivity and transmission rates. Therapeutic interventions for COVID-19 can be further refined by studying the viral genome and its interactions with the host. In one study, researchers built PrismNet, a deep-learning tool [33], to predict 42 host proteins that bound to SARS-CoV-2 RNA and drugs. Antisense oligonucleotides targeting structural elements and FDA-approved agents that inhibited SARS-CoV-2 RNA-binding proteins significantly reduced SARS-CoV-2 infection in human liver and lung tumor cells, thus revealing multiple therapeutic candidates for COVID-19 treatment.
As an RNA virus, the RNA genome itself is the regulatory center that controls and enables its function. RNA molecules fold into tanglesome higher-order structures. Therefore, a more full-scale analysis of the structure of the SARS-CoV-2 RNA genome in infected cells is important for a full understanding of the viral infection and treatment strategies. It has been shown that RNA structural data can be evaluated with cutting-edge deep-learning techniques to precisely predict RNA-binding proteins (RBPs)–RNA binding in vivo by integrating neural network models of binding and matching RNA features in the cell. Overall, the study identifies many single-stranded regions in the SARS-CoV-2 genome, reveals and validates structural elements (including coding regions) with strong coevolutionary support throughout the genome, and shows that functional RNA structural elements can be targeted by small molecule compounds to disrupt viral infectiousness. It can facilitate target discovery and development of antiviral drugs. However, there are some limitations to this study. Firstly, the obtained structural information of SARS-CoV-2 RNA is not ideal and requires further transformation. Secondly, the model can only report the RNA structure information of a single nucleotide, but it cannot directly reveal the higher-order structure information (including the tertiary RNA structure). Thirdly, host factor predictions do not take into account cellular background information such as protein abundance and localization data. In conclusion, studies have demonstrated that several FDA-approved drugs can effectually restrain viral infection in different cells through the SARS-CoV-2 RNA trans-complementation system, but the mechanism of action needs to be further investigated.
In another study, researchers analyzed T-cell receptor sequence (TCR-SEQ) data from the open-access Immunocoding Database to understand immunogenomic differences that may contribute to different clinical outcomes [34]. They identified two cohorts in the database with clinical outcome data reflecting disease severity and used DeepTCR, a multi-instance deep-learning repertoire classifier, to predict severe SARS-CoV-2 infection in patients based on their repertoire sequencing. The study demonstrated that severely infected patients had higher T-cell responses connected with SARS-CoV-2 epitopes and authenticated the epitopes responsible for these responses. The results showed that the clinical course of SARS-CoV-2 infection varied greatly and was related to some antigen-specific responses. Of course, there are many limitations. One limitation is the overfitting of the model, which may be one reason for the lack of cross-validation of the model. Another non-trivial limitation is the limitation of the database, which still lacks clinical data in the actual fitting process.

5. Deep Learning for COVID-19 Control

During this COVID-19 pandemic, unprecedented public health measures have been taken to control the spread of the SARS-CoV-2 virus. One study used deep reinforcement learning, where algorithms were trained to try to find the optimal public health strategy that maximizes the total return to control the spread of COVID-19 [35][36]. The results of the proposed algorithm are analyzed for realistic times and intensities of lockdown and travel restrictions. Researchers have proposed an elementary data-driven approach that utilizes state-of-the-art deep reinforcement learning (RL) algorithms to discover optimal lockdown and travel restriction policies for some countries and regions to reduce the burden of COVID-19. However, the study has some limitations, and early implementation is difficult due to inconsistent testing and reporting of emerging COVID-19 outbreaks and incomplete data. In addition, the study focused only on the health benefits to the population from controlling the spread of COVID-19, without balancing the negative effects of economic and social consequences.
At the same time, given the lack of effective antiviral drugs and restricted medical resources [37], WHO recommends many measures to control infection rates and avoid exhausting limited medical resources [38]. Wearing a mask is one of the non-pharmaceutical interventions that can be used to cut off the main source of SARS-CoV-2 droplets excreted by an infected person [39]. In one study [40], researchers designed a high-precision and real-time technology, called ResNet 50, which can effectively identify non-masked faces in public places to enforce the wearing of masks. Of course, the study has some limitations. Firstly, it cannot tell the difference between a normal mask and a surgical mask. Secondly, the available datasets are small, which makes model training difficult.

6. Deep Learning for COVID-19 Treatment

There is an urgent need for effective treatment for COVID-19. However, the discovery of monotherapies with activity against SARS-CoV-2 has been challenging. Combination therapies play a non-negligible role in antiviral therapy because they improve efficacy and reduce toxicity. In contrast, drug synergies usually occur by inhibiting discrete biological targets. One study has proposed a neural network architecture called ComboNet (similar to protein–protein interaction networks) for the joint learning of drug–target interactions [41][42] and drug–drug synergies [37]. The model consists of two parts; the drug–target interaction module and the target–disease association module. By introducing additional biological information, the model significantly outperforms previous approaches in terms of collaborative prediction accuracy. Despite limited training data on drug combinations, the study empirically validated the model predictions and found two drug combinations: remdesivir and IQ-1S (an effective and specific c-Jun N-Terminal Kinase Inhibitor), and remdesivir and reserpine, both of which showed strong in vitro anti-SARS-CoV-2 synergies.
In addition to combination therapy, drug reuse offers a promising approach to the development of COVID-19 prevention and control strategies. One study reported an integrated, web-based deep-learning approach for identifying reusable drugs for COVID-19 (called Coronavirus-knowledge graph embedding, Cov-KGE) [43]. The researchers built a comprehensive knowledge graph that used Amazon’s Amazon Web Services computing resources and Web-based deep-learning frameworks to identify 41 reusable drugs (including indomethacin, dexamethasone, toremifene, and niclosamide) whose therapeutic association with COVID-19 has been validated using transcriptional and proteomic data from human cells infected with SARS-CoV-2 and data from ongoing clinical trials.
Of the 41 drug candidates, 9 are in or have been in clinical trials for COVID-19, including thalidomide, ribavirin, methylprednisolone, umifenovir, suramin, tetrandrine, dexamethasone, azithromycin, and lopinavir. Researchers also provides a powerful, integrated deep-learning approach for the rapid identification of reusable potential therapeutics for COVID-19. However, it is important to note that all predictive drugs must be tested in randomized clinical trials before they can be used in patients with COVID-19.
In the COVID-19 era, an intelligent medication behavior monitoring system (MBMS) is also needed to monitor patients stably [44], because many patients cannot easily go to the hospital, or the medical staff in the hospital cannot monitor the patient’s condition in real-time. Similar to the use of the Internet of Things (IoT) for electrocardiogram (ECG) systems to predict cardiovascular disease and electronic medical records [45][46], one study designed a medication behavior monitoring system using IoT and deep learning to effectively detect various activities of patients, avoid perceptual errors, and improve user experience. The system uses a human activity identification scheme to identify the medication status in a timely manner and proactively relay various notices to the patient’s mobile device. Information measured on a patient’s medication status is transmitted to doctors, allowing them to perform remote treatments on a regular basis. Experiments show that the proposed system can automatically detect all medication behaviors of patients and inform doctors effectively, improving the accuracy of monitoring the medication behavior of patients. The system is applied to COVID-19 treatment, which can improve the treatment process of patients and collect a large amount of medical data and patient disease data through various sensors. It can solve various problems such as remote treatment and rapid recovery of patients so that patients can receive treatment quickly and comfortably.
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Figure 2. The deep-learning methods for COVID-19. LASSO: least absolute shrinkage and selection operator; RL: reinforcement learning; Cov-KGE: Coronavirus-knowledge graph embedding; MBMS: medication behavior monitoring system; Grad-CAM: Gradient-Weighted Class Activation Mapping; GANs: generative adversarial networks; M-LSTM: multivariate long short-term memory; CPH: the Cox proportional hazards model; DL: deep learning; MLP: a multilayer perceptron; CNN: convolutional neural network; RNN: recurrent neural network; GNN: graph neural network.

This entry is adapted from the peer-reviewed paper 10.3390/math11061279

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