key: cord-1041427-cyhnz1vd
authors: Bouchareb, Yassine; Moradi Khaniabadi, Pegah; Al Kindi, Faiza; Al Dhuhli, Humoud; Shiri, Isaac; Zaidi, Habib; Rahmim, Arman
title: Artificial Intelligence-Driven Assessment of Radiological Images for COVID-19
date: 2021-07-21
journal: Comput Biol Med
DOI: 10.1016/j.compbiomed.2021.104665
sha: 4b11dc5f77d49f2d201ed297d9052ee373a58203
doc_id: 1041427
cord_uid: cyhnz1vd

Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

utilizing neural networks [21] [22] [23] [24] [25] . Highly mature studies utilizing DL algorithms for detection and/or reporting the severity of COVID-19 infections are in references [26] [27] [28] [29] [30] [31] [32] . Moreover, DL technically does not require segmented ROIs, and, if large enough datasets are provided, is able to focus on areas of importance. Each of these methods (radiomics and DL) has its own advantages (the former working better for small/medium datasets; the latter working better on large datasets). Other than that, hybrid methods integrating the two approaches ("deep radiomics") have also been explored to utilize quantitative data extracted/derived from medical images [33] . Examples of this approach include initial generation of radiomics images at the voxel level fed into deep neural networks (DNNs), or alternatively, extraction of deep features as generated by DNNs (e.g., in the fully connected layer) combined with machine learning algorithms.

Radiomics (and the use of ML techniques to combine radiomics features into a model; i.e., radiomics signature) may be applied to the acquired datasets to enhance the assessment of diseases. However, linking radiomics (i.e., process of extracting quantitative image features) to biological or pathophysiological processes being investigated remains challenging, and has in the past hindered the translation of radiomics into clinical practice despite providing promising results into tissue characterization; this issue, however, is receiving further attention in recent years and is an active area of research [14] . Beyond the direct impact of COVID-19 AI-based methods for the diagnosis and prognosis of SARS-CoV-2 virus, an explicit research pathway may lead to establishment of a comprehensive prognostic approach to fight the spread of COVID-19.

To this end, Born et al. [34] performed a systematic meta-analysis focusing on ML-based COVID-19 utilizing CXR, CT, and ultrasound images aiming to identify the most relevant articles. Bhattacharya et al. [35] focused on reviewing DL algorithms which were utilized in COVID-19 analysis and an overview of DL and its clinical impact over the last decade.

Elsewhere, Shoeibi et al. [36] performed a complete review of DL techniques and tools for COVID-19 detection and lungs segmentation. Moreover, the challenges related to the automated detection of coronavirus infections using DL methods were reported. The authors summarized the COVID-19 prevalence in several parts of the world. Recently, Suri et al. [37] investigated a variety of comorbidities and their associated risks in acute respiratory distress syndrome and mortality. They elaborated on AI architectures and their extension from pre-COVID-19 to post-COVID-19 and the views of seven school-of-though were summarised. It is worth mentioning that non-imaging information obtained from genomics, proteomics, J o u r n a l P r e -p r o o f lipidomics, and transcriptomics, combined with AI-based approaches, can be valuable for the diagnosis and prognosis of COVID-19 [38, 39] . To have a clearer picture on deploying AIbased approaches in clinical practice to help the fight against COVID-19, Fig. 1 illustrates the workflow of conventional methods (non-AI methods) and AI-based methods. 

AI methods utilize machine learning (ML) approaches (as applied to radiomics features) vs.

deep learning (DL) algorithms as directly applied to the images [4, 5] . ML and DL have played significant roles to mine, interpret, and identify data patterns. ML models utilized for the diagnosis and/or prognosis of COVID-19 which were reported have included: Support Vector Machine (SVM) [40] [41] [42] , Random Forest (RF) [43, 44] , Decision tree (DT) [45, 46] , and Logistic regression (LR) [47, 48] . DL models frequently employed have been convolutional neural networks (CNNs) [49, 50] and recurrent neural networks (RNNs) [51, 52] . It was successfully demonstrated that AI-based model, combining CT/CXR modality and other clinical information, could be useful in screening COVID-19 that does not require radiologist input or physical tests. Xia et al. [53] performed a DL-based approach utilizing a classifier that combines clinical variables such as patient demographics, symptoms (cough, fever, sore throat, etc.), signs of infection (e.g., enlarged tonsils and lymph nodes), underlying diseases (e.g., hypertension, diabetes, etc.), and blood results with CXR data to distinguish COVID-19 from viral pneumonia in a simple, efficient, inexpensive, and accurate way. A recent study by Shiri et al. [54] revealed that integrating radiomics features with demographics and clinical data J o u r n a l P r e -p r o o f (gender, age, weight, height, BMI, medical history of comorbidities and vital signs), laboratory features (blood tests) and radiological data (scoring by radiologists) can help the prediction of overall survival in COVID-19 patients.

Overall, AI is a valuable technology for early detection of COVID-19 infections and proper health monitoring. CT and CXR have been identified and used as the imaging modalities of choice for the prognosis of COVID-19 infections. The following two sections (II.1 and II.2) describe AI-based signatures of COVID-19 studies utilizing CXR and CT scans

Potential trends and information derived from the X-ray radiographs scans has proved to be useful in COVID-19 diagnosis since pulmonary infections have been detected through X-ray images [55] . CXR are commonly performed first and play role as an alternative viable choice when CT scanners are not available for fast diagnosis and monitoring the progression of COVID-19 cases [56, 57] . In COVID-19 CXR findings are: pneumonia, namely bilateral peripheral and/or, subpleural ground glass opacity (GGO) and/or consolidation, unilateral nonsegmental/lobar ground-glass or consolidative opacities or multifocal groundglass/consolidative opacities without many particular distributions (Fig. 2) . Moreover, using public CXR databases increased remarkably the number of AI-based COVID-19 studies that aimed to detect, follow, and predict outcome of SARS-CoV-2 infections [58] . DL-based approaches allowing automated analysis of CXR images significantly accelerated the analysis and processing time and helped speed-up the identification and control of the spread of COVID-19 [59] . Bukhari J o u r n a l P r e -p r o o f Training a CNN model from scratch necessitates a large amount of training data and technical skills to determine the best model architecture for optimal convergence. Additionally, this requires time-consuming annotations by radiology experts. Due to computational requirements and memory constraints, CNN training takes a long time. Transfer learning has the advantage to decrease computational complexity and to speed-up the process. Basu et al. [62] successfully classified CXR images (accuracy of 90%) into four classes: normal, pneumonia, other disease, and COVID-19, using a CNN pre-trained on normal and disease classes that were obtained from the National Institutes of Health (NIH) Chest X-ray freelyaccessible database. The activation map was used to identify regions where the emphasis was classification of features. The average detection precision was found to be 95.2%.

Hall et al. [63] obtained on overall accuracy of 91.2% when they pertained 135 CXR of COVID-19 and 320 CXR of viral and bacterial pneumonia by a deep CNN (Resnet50 software). They suggested and recommended that CXR is an inexpensive, accurate and fast imaging modality for diagnosis of COVID-19. Wang et al. [64] proposed COVID-Net as a new deep learning architecture for prediction of COVID-19 disease using CXR. A dataset with a total of 5896 CXR images (358 COVID-19 and 5538 non-COVID-19) was studied. In total, four classes of cases were considered: (a) normal, (b) bacterial infection, (c) non-COVID infection, and (d) COVID-19 infection. Given the quantity of COVID-19 images collected, the distribution of inter-class images among their training sets and among their validation sets was highly unbalanced. They leveraged the principles of residual architecture design. A 93% accuracy was obtained by the COVID-Net architecture emphasizing the ability to utilize new DNN architectures, and reflecting ever-evolving efforts on dedicated DL architectures.

Zhang et al. [13] performed advanced analysis using a DNN (CV19-Net) to differentiate COVID-19 from non-COVID-19 infections, on a total of 11105 CXR images (2060 COVID-19 cases and 3148 non-COVID-19 cases). State-of-art algorithms for the test set, CV19-Net achieved a sensitivity of 88% and a specificity of 79% by using a high-sensitivity operating threshold, and a sensitivity of 78% and a specificity of 89% by using a high-specificity operating threshold. They evaluated the performance of CV19-Net by choosing 500 CXR that were examined by the both CV19-Net and three radiologists. They reported an AUC of 0.90 for CV19-Net and an AUC of 0.85 obtained by radiologists. J o u r n a l P r e -p r o o f

As COVID-19 continues to infect people all around the world, beside real-time reverse transcription polymerase chain reaction, CT plays an essential role in faster diagnosis. Various manifestations of COVID-19 on chest CT images (particularly, consolidation, GGO, or a mixture of GGO and consolidation) were important to distinguish between infections of the lungs [65] . Hence, early COVID-19 screening, differential diagnosis (see Fig. 3 ), and disease severity assessment/follow-up was achieved from reading chest CT scans (refer to Fig. 4) .

Moreover, CT images helped radiologists to visualize the effects of COVID-19 by means of 3D printing technology [66] .

The most common CT findings, as lesion features, can be categorized into: (1) small peripheral/subpleural, bilateral, ground-glass opacities with/without consolidation; (2) crazypaving pattern; (3) air bronchogram sign; (4) consolidation; (5) linear opacities; and (6) bronchial wall thickening [67] . In addition to detecting COVID-19 using deep learning methods, Shiri et al. [68] proposed a deep learning based algorithms for dose reduction in chest CT images. They implemented a residual DNN to generate high quality images from ultra-low dose CT images. Model were built on 970 chest CT images and evaluated on 170 external validation set of COVID-19 patients. They reported 89% dose reduction in CT images with properly recovering most common lesion features in COVID-19 images. Shan et al. [69] suggested a method for reducing the prognosis time by automated methods of delineating CT images from 1 to 5 hours to 4 to 3 minutes compared with manual method. They used a humanin-the-loop strategy to accelerate the delineation of CT images. The authors reported that this auto contoured regions could assist radiologists for their annotation refinements. Song et al. [70] conducted a deep leaning-based study on CT images of 88 COVID-19 and normal cases.

They reported sensitivity of 93% the model to distinguish COVID-19 from pneumonia. Ai et al. [71] emphasized that with RT-PCR as a reference, the sensitivity of chest CT imaging for COVID-19 was 97%. Lin et al. [71] performed retrospective and multi-centre DL-based model study utilizing COVID-19 detection neural network (COVNet) for feature extraction from CT scans for COVID-19 diagnosis purpose. Community-acquired pneumonia and other nonpneumonia CT exams were included to study of the model. The pre-exam sensitivity and specificity in detecting community-acquired pneumonia in the test via COVNet were reported to be 87% and 92% respectively. Although, CT has been reported as a most accurate tool to detect the COVID-19 [72] , Elsewhere, Li and his colleagues Xia illustrated the dark side of CT scans on diagnosis of COVID-19. They found that CT findings of COVID-19 overlapped with the CT features of adenovirus infection due to this limitation in distinguishing between viruses and identification the infection patterns of other viruses [73] . In most recent study, Shiri et al. [54] implemented a holistic radiomics model with combining CT radiomics features (extracted from lung, and 

AI-based analysis has also been shown to have a role in detecting COVID-19. More Table 1 . Image derived metrics such as radiomics features are sensitive to image acquisition settings, reconstruction algorithms and image processing methods [68] . J o u r n a l P r e -p r o o f

Image segmentation is an essential processing step that helps improve the accuracy image analysis and clinical reports [19] . By using image segmentation techniques, an image is divided into specific groups of pixels, assigned labels (lung regions, lesions, etc) and classified further according to these labels. The labels generated by image segmentation are then provided as an input to AI-based methods. Subsequently, radiomics features can be calculated from the segmented 2D/3D ROI in radiomics based analyses. In relation to the target ROIs, the segmentation method in AI-based COVID-19 are classified into, (a) the lung-region-oriented method, which is basically able to separate the entire lung and lung lobes in CT/CXR images;

(b) the lung-lesion-oriented method; which tries to distinguish lesion in the whole lung from the regions. By segmenting lesions and healthy lungs in CT images, volume of infection and relative volume (lesion/lung) could be calculated, which further could be used as prognostic and severity scoring of COVID-19 patients. Automatic and semiautomatic segmentation approaches can either define features as COVID-19 or non-COVID-19.

Overall, segmentation techniques are specifically divided into four categories; (a) manualbased segmentation is defined as the delineation of the contours of anatomical regions that is performed by experts (e.g. radiologists, pathologists) [74] ; (b) model-based segmentation is defined as the assignment of labels to pixels or voxels by matching the a priori known object model to the image data [75] ; (c) DL-based segmentation (dominantly CNN-based) as used for automated feature extraction; and (d) hybrid segmentation methods which combines conventional and DL-based methods [76, 77] . Segmentation of the lungs in COVID-19 infected cases consists of delineating the borders of the anatomical structures of lung or pneumonia lesions with computer-assisted contouring. It delineates regions of interest (ROIs) or volumes of interest (VOIs) in COVID-19 CT or CXR images; these are commonly: whole lungs, lung lobes, trachea, lung lesions, bronchus and pneumonia lesions. For segmentation, different methods were utilized including, thresholding, region-based, clustering-based, watershedbased algorithms [78] [79] [80] [81] [82] . Due to high variety of shape, size, boundary, type and manifestation of lesion in COVID-19 conventional algorithms failed to properly segment the lung and pneumonia lesions. In the COVID-19 pandemic, several ML-based algorithm were proposed for lung and lesion segmentation of COVID-19 radiological images [83, 84] . Support Vector Machine (SVM) [85] is an ML method that has reported widely for supervised segmentation in radiomics-based COVID-19 studies [86, 87] .

Unlike conventional segmentation methods, unsupervised segmentation methods have typically relied on intensity or gradient analyses of the image via various strategies (i.e, using Inf-Net for COVID-19 CT images) to delineate the contours of the anatomical areas in the image. Such methods can as such dive deeper in considering several resolution levels in medical images. It is also possible to use unsupervised deep learning models for segmentation.

Each and every layer will learn information from the CXR/CT images depending on the content of images or feature map. Deng-Ping et al. [88] proposed Inf-Net to determine coarse regions, which were followed by applying implicit models that boosted boundaries detection. They also used semi-supervised segmentation on COVID-19 SemiSeg and real CT images to render most of the unlabelled data. Surprisingly, they observed that the semi-supervised system enhanced volume learning capabilities compared to other cutting-edge programs.

To enhance the accuracy of predicted model utilizing two architecture methods (U-Net and Resnet-50) for segmentation of CT images of lung abnormalities was suggested. The proposed method enables the segmentation of ROIs and classify CT scans as COVID-19 and non-COVID-19 cases [5] [6] [7] [8] . As it was mentioned above, U-Net and its variant have been developed and have achieved fair segmentations in COVID-19 CT/CXR images. Çiçek et al. [35] recommended the 3D U-Net that utilises the inter-slice info by replacing the layers in wellknown U-Net method in 3D format. A VB-Net was used by Shan et al. [89] for more effective segmentation. Elsewhere, Tang et al. [86] adopted a VB-net [7] to perform accurate segmentation of the whole lungs and lung lesions from CT images. Using U-Net with the initial seeds provided by a radiologist, Qi et al. [37] presented segmentation of lesions in the lungs (see Table 2 ).

To evaluate segmentation accuracy, dice similarity coefficient (DSC) as a metric has been employed to determine the overlap between automatically segmented COVID-19 infection regions in the lungs and gold-standard manual delineations. Shan et al. [69] reported 92% DSC for automated segmentation of COVID-19 infection using VB-Net. Elsewhere, Shan et al [90] utilized DL-based method for determining the severity of COVID-19 using 549 CT scans of COVID-19 patients. They applied VB-Net, an automated segmentation tool, and reported 92% DSC. In a more recent study, Shiri et al. (95% CI, −0.12 -0.18) and −0.18±3.4% (95% CI, −0.8 -0.44) for the lung and lesions, respectively. They also reported relative errors less than 5% for first-order and shape radiomics features in both lung and lesions. Figure 6 provides an example comparing manual segmentation performed by a radiologist and COLI-NET output for whole lung and lesion segmentation for COVID-19 patients at different stage (from mild to severe). 

For the radiomics subset of AI-methods, effective feature extraction is a pillar of radiomics towards learning rich and informative representations from raw input data to provide accurate and robust results. Knowledge of the different types of radiomics features and core principals may facilitate interpretation of results and preselection of features for specific application. To extract the desired radiomics features of COVID-19, segmentation and processing of images should be performed accurately. In other words, feature extraction indicates the computation of features, where descriptors are used to determine attributes of the grey levels within the 2D/3D ROI [92] . Features have to be obtained so that they express the complexity of each volume as best possible, but cannot be excessively redundant or complex. To date, several techniques and algorithms have been applied to extract COVID-19 features, although no agreement exists about a standard method (see Fig. 5 and Table 2 ). Different types of COVID-19 explicit radiomics features were identified, the most commonly found ones are shape, statistics, histogram, and texture features including Gray-Level Co-occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), and Gray Level Size Zone Matrix (GLSZM). Moreover, the extraction depends on the amount of data, and different types of filters such as Wavelet decomposition or Gaussian filters that are carried out to identify the key points in the images during this step of the radiomics pipeline. Various sets of features can be studied and combined for developing the suitable model for diagnostic and prognostic purpose [40] . In DL approaches, features extraction is achieved implicitly via DNNs. Using these approaches, relevant features are extracted directly and automatically from the raw pixels of the original CT and CXR images [93] [94] [95] (see Table 2 ).

Alqudah et al. [96] utilizes SVM and RF for detection of COVID-19 at an early stage. They adopted a deep radiomics model for feature extraction. Specifically, they extracted features from the fully connected layer in a CNN, followed by use of machine learning to combine these deep features, to build a model to distinguish between COVID-19 vs. non-COVID-19 cases.

Ghoshal and Tucker [97] used a Bayesian CNN (BCNN) method to distinguish the COVID-19 infection using CXRs. A classification accuracy of 90% was reported. Salman et al. [98] employed a trained CNN for identifying the COVID-19 using CXRs. The sensitivity and specificity achieved from the model were 100% and 100%, respectively. Farooq and Hafeez The training data is defined as a collection of instances, represented by a set of patients, each with a collection of features and a desired category label. The classifier evaluates the training data and derives a model that can be used to predict the labels from the input features. For this process, AI approaches and statistical approaches were employed. At the other end, the data mining spectrum are hypothesis-driven methods that cluster features in line with information of content. By getting used of the advantages of these two approaches, the best models can be J o u r n a l P r e -p r o o f selected and further emphases in a particular medical context and, therefore, well-defined endpoint will be achieved. There are two categories of methods for dimensionality reduction: feature selection and feature extraction, which we discuss next. The former, extract a subset of features from a given set of (large) features. The latter, combines existing features into a reduced number of new features/dimensions; e.g. Principal Component Analysis (PCA) [102] .

This term ("feature extraction"), as we use it here, should not be confused with extraction of hand-crafted radiomics features, which can also be referred to as feature extraction. In the present use, it is in the context of dimensionality reduction that it is used. The filter-based method utilizes the data related specifications to assess the merits of the feature subset. The wrapper-based methods employ a specific classifier to estimate the significant features. Feature selection approaches using special group of filters tend to simultaneously select highly predictive but uncorrelated features. One of such filter is the Maximum Relevance Minimum Redundancy (MRMR) algorithm, which was first developed for feature selection of microarray data. Fu et al. [103] employed MRMR to find out the high correlation and low redundancy features from 2 sets of features (radiologists A and B) which were obtained to construct the COVID-19 radiomics signature.

Elsewhere, Li et al. [104] applied the Mann-Whitney U test to determine the correlation between features (extracted from 64 CT images of COVID-19 cases) and severity score. Then, the MRMR algorithm utilized to rank features according to their relevance to severity to J o u r n a l P r e -p r o o f select optimal feature subset. Rafid et al. [105] adopted filter-based and wrapper-based (hybrid method) to select the most relevant features. The MRMR and Double Input Symmetrical Relevance (in total 1144 features were obtained by discrete wavelet transform and CNN) feature selection methods along with recurrent feature elimination (RFE) techniques were adopted.

Unsupervised techniques for feature reduction are divided into linear and nonlinear methods that aim to only keep low number of features. By adopting these methods, the new reduced set of features that was created from a combination of original features will be feed into the analysis. Hence, the original features will be discarded. In other words, the features that do not provide additional information will be removed. PCA is a multivariate statistical procedure that J o u r n a l P r e -p r o o f

The dominant approaches used in AI are ML and DL, which offer fast, automated, decisive strategies to enable improved assessment of COVID-19 infections. Using AI techniques, scientists succeeded in extracting features from COVID-19 CT and CXR datasets to extract findings that are specific and highly sensitive for early COVID-19 detection, prognosis and A weakly-supervised DL-based software system was tested by Zheng et al. [78] to detect COVID-19 using 3D CT images. A pre-trained UNet was used for lung region segmentation.

The results showed 90% sensitivity and 91% specificity. The algorithm achieved an accuracy of 90% to define COVID-19 and non-COVID groups. Ioannis and Tazani [118] extracted features from numerous images of CXR (COVID-19 disease, common and normal bacterial pneumonia). The findings indicate that DL using CXR can extract essential biomarkers related to COVID-19 disease. A 96.8% accuracy, 98.7% sensitivity, and 96.5% specificity were reports in this study. Forest-associated pneumonia in the research datasets, respectively. A sensitivity and specificity of 100% and 89% was achieved by the logistic regression model, and similar performance was shown by the RF model with a sensitivity and specificity of 75% and 100% in the test datasets.

Liu et al. [116] A prognostic method for predicting poor results in COVID-19 based on CT imaging was suggested by Wu et al. [122] . A total of 492 patients were classified into (a) the early-phase group (CT scans one week after onset of symptoms); (b) the late-phase group (CT scans one week after onset of symptoms). The radiomics signature (RadScore software) was developed to build the low-pass Gaussian filter, and LASSO reduction methods/ classification methods.

Afterwards, the clinical model and the clinic-radiomics signature (CrrScore), was stablished by performing a Fine-Gray competing risk regression. They found that in group (a), the CrrScore estimated 85% poorness, and predicted the probability of 28-day poor results of 86.2%. In group (b), the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (88.5%), and 28-day poor outcome probability (97.6%).

The first significant challenge in dealing with patients with COVID-19 symptoms is to identify and prioritize cases so that the physician can isolate infected patients as soon as possible. For COVID-19 cases, a triage algorithm needs to prioritize those who require emergency medical care, according to the severity of infection. Radiomics models may assist radiologists and clinicians in making fast and accurate diagnosis and prognosis, to ensure appropriate clinical management and resources allocation. Moreover, radiomics models have the potential to distinguish COVID-19 from pneumonia caused by other etiologies. The major clinical impact highlighted by radiomics-based COVID-19 studies were in the areas of: screening of infection, identification and detection, prediction of disease progression, and analysis of survival rate.

The following subsections summarize the most relevant studies for screening, diagnosis, prediction, and severity quantification of COVID-19 using AI-based methods

Screening patients attending emergency departments for COVID-19 at time of overwhelming outbreak using RT-PCR is challenging process as that might take up to 24 hours to obtain the results. Screening with Chest CT was adopted in many centres across the globe. performed a preliminary study on CT scans using ML methods. The reported results were promising for differentiating COVID-19 and H1N1 influenza. Elsewhere, Guiot et al. [110] developed a AI framework to differentiate COVID-19 from other routine clinical conditions in a fully automated fashion, hence providing rapid accurate diagnosis of patients suspected of COVID-19 infections to enable early intervention.

Providing an accurate prediction of the evolution of SARS-CoV-2 infections is expected to facilitate timely implementation of isolation procedures and early intervention as well as [133] reported a risk score-based approach that could predict the mortality of COVID-19 patients by more than 12 days with more than 90% accuracy across all cohorts.

Severity quantification is a common measure to assess patients' health condition. A new AI-based method that automatically performs 3D segmentation and quantifies abnormal CT patterns in COVID-19 (e.g., GGO and consolidation) was introduced by Changanti et al. [134] .

Based on DL and deep reinforcement learning, the method provided combined assessments of lung involvement to assess COVID-19 abnormalities (percentage of lungs involvement) and the existence of large opacities (severity scores). The method offers the potential of a better management of patients and clinical resources. Using a commercially available DL-based technology, Huang et al. [135] determined that quantification of lung opacification in COVID- [136, 137] . Future studies should address the concerns raised. Some potential avenues are discussed below.

Translation of AI-based methods to routine clinical practice is hampered by the proper validation of existing research studies. Insufficient data due to small sample sizes and toy datasets for different types or stages of disease or infection (e.g. mycoplasma infections) can induce significant selection bias as well as imbalanced COVID-19 dataset [63, 138] . A study on a subgroup of early COVID-19 paediatric patients was not successful due to lack of sufficient CT scans [28] .

Five common categories of COVID-19 severity based on total lungs involvements (LI) (score 0: 0% LI; score 1: 5% LI; score 2: 5%-25% LI; score 3: 26%-50% LI; score 4: 51%-75% LI; as well as score 5: 75% lobar involvement) have been considered. The severity scores are classified as negative, moderate, non-severe, critical and extreme [139] . Elsewhere, given small sample size of COVID-19 paediatric patients were less in some of these categories, only two categories were examined [42] . A solution is to use pertained networks (transfer learning) followed by fine-tuning using COVID-19 datasets. However, employing pre-trained networks (commonly for non-medical applications) in real medical applications is still challenging.

For different types of pneumonia only one case was reported and therefore, the characteristics comparison between COVID-19 and other types of pneumonia failed [26, 28, 37] . Overall, for proper generalizability of models, multi-centric, large datasets are required.

Further, using appropriate data augmentation, transfer learning based on other COVID-19 models, and federated learning frameworks, the accuracy of the models might be enhanced [25] . To attain adequate balance classes for AI analysis, some techniques such as modified loss J o u r n a l P r e -p r o o f functions or resampling utilizing Synthetic Minority Oversampling Technique (SMOT), downsampling and up-sampling might be helpful.

Apart from the issue of sample size, imaging protocols are often not standardized for such studies. The effect of these settings on radiomics features was investigated to minimize their influence by eliminating features that are sensitive (i.e. not reproducible) to those parameters [140] . Different brands of imaging scanners are available in the market, rendering identical performances and standard protocols of scanning for COVID-19 patients highly problematic across different hospitals and imaging centres. Alternatively, harmonization of acquired imaging datasets might be helpful for data collection at different centers [141] .

It was reported that the impact of the virus on the lungs is highly related to the host factor [142] . The CT data on its own is not sufficient to identify the types of viral pneumonia. A study stated that clinical and clinic-radiomics combined model results in better diagnosis of COVID-19 pneumonia compare to COVID-19 reporting and data system (CO-RADS) only model [116] . A recent study revealed that AI-based prediction modeling using CXR radiographs was insufficient unless diagnostic test results such as RT-PCR are also available [143] . Hence, clinical features and laboratory examination data are required to be investigated in addition to imaging features [26] . In other words, AI-based models should not merely rely on images, and combination of imaging data with clinical information enhances model applicability.

Segmentation can significantly impact feature extraction and hence the classification and clinical outcomes. The expertise of the operator in semi-automated or manual methods is a key explicit feature and may dictate the occurrence of the outcomes. Chest imaging scans of COVID-19 infected patients that have small ground-glass lesions could be missed when the ROI is automatically delineated using existing methods. However, fully automated segmentation methods based on deep learning have the potential to replace less automated methods, if large, accurate reference truth datasets are generated for training. Overall, 3D segmentation networks and the adoption of precise ground-truth annotated by radiologists is desirable and more efficient for explicit radiomics feature extraction and analysis [25] .

Meticulous segmentation of COVID-19 images makes extraction of the radiomics matrix a great challenge for accurate labelling and clustering of regions [91] . Nevertheless, AI-based analysis is used on a variety of datasets, including labelled, non-labelled, mixture or small labelled and huge number of non-labelled data. Depending on the objectives and expected outcomes of the studies, the lack of accurate segmentation models can be compensated for by employing deep learning methods. In particular, manual segmentation can be replaced utilizing large training datasets, neural networks and evaluation algorithms. The extracted features can be hence identified to facilitate rapid and more accurate diagnosis as well as timely management of COVID-19 patients.

Sharing COVID-19 scan details in publications that focus on CT and CXR datasets has been critical for COVID-19 first-line diagnosis during the early stages of the pandemic [144] . Later, the availability of free online COVID-19 CT and CXR images, contributed to proliferation of AI approaches and high enthusiasm about fostering diagnosis and prognosis for COVID-19

patients [29] . Subsequently, number of AI-based COVID-19 publications has increased rapidly. Public datasets vary remarkably in terms of ancillary datasets that directly enhance the performance of the AI models. The majority of datasets are still private, and publications based on public datasets are neither comprehensive enough nor clinically useful [136] . To overcome this hurdle in the application of AI-based COVID-19 studies, there is significant need for ongoing construction and expansion of COVID-19 database from carefully curated imaging and non-imaging data. AI-based studies were highlighted to help initiate further efforts to overcome current limitations. :   Table 1 J o u r n a l P r e -p r o o f 

Differential diagnosis for suspected cases of coronavirus disease. 2019: a retrospective study

COVID-19 pneumonia: a review of typical CT findings and differential diagnosis

A theranostic approach based on radiolabeled antiviral drugs, antibodies and CRISPR-associated proteins for early detection and treatment of SARS-CoV-2 disease

Machine learning: a predication model of outcome of SARS-CoV-2 pneumonia

Testing for SARS-CoV-2 (COVID-19): a systematic review and clinical guide to molecular and serological in-vitro diagnostic assays. Reproductive biomedicine online

Focused role of nanoparticles against COVID-19: Diagnosis and treatment. Photodiagnosis and Photodynamic Therapy

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis

Review of analytical performance of COVID-19 detection methods

Detection of covid-19 from chest x-ray images using artificial intelligence: An early review

Determination of disease severity in COVID-19 patients using deep learning in chest X-ray images

The promise of artificial intelligence and deep learning in PET and SPECT imaging

Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy

Diagnosis of coronavirus disease 2019 pneumonia by using chest radiography: Value of artificial intelligence

The biological meaning of radiomic features

Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients

Repeatability of radiomic features in magnetic resonance imaging of glioblastoma: Test-retest and image registration analyses

Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning

Integration of PET/CT Radiomics and Semantic Features for Differentiation between Active Pulmonary Tuberculosis and Lung Cancer

Machine Learning Methods for Optimal Radiomics-Based Differentiation Between Recurrence and Inflammation: Application to Nasopharyngeal Carcinoma Post-therapy PET/CT Images

Cardiac SPECT radiomic features repeatability and reproducibility: A multi-scanner phantom study

Deep-JASC: joint attenuation and scatter correction in whole-body (18)F-FDG PET using a deep residual network

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET

Deep learning-assisted ultrafast/low-dose whole-body PET/CT imaging

Whole-body voxel-based internal dosimetry using deep learning

Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks

Artificial intelligence-enabled rapid diagnosis of patients with COVID-19. nature medicine

Dual-sampling attention network for diagnosis of COVID-19 from community acquired pneumonia

Prior-attention residual learning for more discriminative COVID-19 screening in CT images

Artificial intelligence augmentation of radiologist performance in distinguishing COVID-19 from pneumonia of other origin at chest CT

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images

Automated assessment of CO-RADS and chest CT severity scores in patients with suspected COVID-19 using artificial intelligence

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

On the Role of Artificial Intelligence in Medical Imaging of COVID-19. medRxiv

Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey. Sustainable cities and society

Automated detection and forecasting of covid-19 using deep learning techniques: A review

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

Machine learning assisted prediction of prognostic biomarkers associated with COVID-19, using clinical and proteomics data

Plasma Metabolomic Profiles and Clinical Features in Recovered COVID-19 Patients Without Previous Underlying Diseases 3 Months After Discharge

Coronavirus (covid-19) classification using ct images by machine learning methods

Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classification

A machine learningbased model for survival prediction in patients with severe COVID-19 infection

Potential neutralizing antibodies discovered for novel corona virus using machine learning

COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning

Data-driven discovery of a clinical route for severity detection of COVID-19 paediatric cases

Finding an accurate early forecasting model from small dataset: A case of 2019-ncov novel coronavirus outbreak

Development and utilization of an intelligent application for aiding COVID-19 diagnosis. medRxiv

Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study. medRxiv

Predicting Mechanical Ventilation Requirement and Mortality

Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images

Severity Assessment and Progression Prediction of COVID-19 Patients based on the LesionEncoder Framework and Chest CT. medRxiv

A rapid screening classifier for diagnosing COVID-19

Machine learning-based prognostic modeling using clinical data and quantitative radiomic features from chest CT images in COVID-19 patients

Diagnostic performance of chest X-ray for COVID-19 pneumonia during the SARS-CoV-2 pandemic in Lombardy

COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. La radiologia medica

Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review

Deep Learning on Chest X-ray Images to Detect and Evaluate Pneumonia Cases at the Era of COVID-19

Automated deep transfer learningbased approach for detection of COVID-19 infection in chest X-rays

The diagnostic evaluation of Convolutional Neural Network (CNN) for the assessment of chest X-ray of patients infected with COVID-19. medRxiv

Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases

Deep Learning for Screening COVID-19 using Chest X-Ray Images

Finding covid-19 from chest x-rays using deep learning on a small dataset

A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images

CT imaging and differential diagnosis of COVID-19

Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review

Ultra-lowdose chest CT imaging of COVID-19 patients using a deep residual neural network

Lung infection quantification of covid-19 in ct images with deep learning

Deep learning enables accurate diagnosis of novel coronavirus (COVID-19) with CT images. medRxiv

Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT

Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT

Coronavirus disease 2019 (COVID-19): role of chest CT in diagnosis and management

COVID-19 Chest CT Image Segmentation Network by Multi-Scale Fusion and Enhancement Operations

Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism

MFP-Unet: A novel deep learning based approach for left ventricle segmentation in echocardiography

Automatic Segmentation of COVID-19 CT Slices Based on Dual Attention and Hybrid Dilated Convolution

Deep learning-based detection for

Towards efficient covid-19 ct annotation: A benchmark for lung and infection segmentation

CT radiomics can help screen the coronavirus disease 2019 (COVID-19): a preliminary study

Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images

Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system

Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

Abnormal Lung Quantification in Chest CT Images of COVID-19 Patients with Deep Learning and its Application to Severity Prediction

COLI-NET: Fully Automated COVID-19 Lung and Infection Pneumonia Lesion Detection and Segmentation from

The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping

A novel approach of CT images feature analysis and prediction to screen for corona virus disease (COVID-19)

Coronavirus (covid-19) classification using deep features fusion and ranking technique. Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach

A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in medicine unlocked

Covid-2019 detection using x-ray images and artificial intelligence hybrid systems

Estimating uncertainty and interpretability in deep learning for coronavirus

Covid-19 detection using artificial intelligence

A Deep Learning Framework for Screening of COVID19 from Radiographs

Automated methods for detection and classification pneumonia based on x-ray images using deep learning

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Exploratory Analysis of COVID-19 Patients Usingprincipal Component Analysis, Feature Selection and Predictive Algorithms

A Novel Machine Learning-derived Radiomic Signature of the Whole Lung Differentiates Stable From Progressive COVID-19 Infection: A Retrospective Cohort Study

Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics

Covid-19 Detection in Chest X-ray Through Random Forest Classifier using a Hybridization of Deep CNN and DWT Optimized Features

Transfer learning based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data

A machine learning-based framework for diagnosis of COVID-19 from chest X-ray images

An automated machine learning model to assist in the diagnosis of COVID-19 infection in chest x-ray images. medRxiv

MULTI-DEEP: A novel CAD system for coronavirus (COVID-19) diagnosis from CT images using multiple convolution neural networks

Development and validation of an automated radiomic CT signature for detecting COVID-19. medRxiv

StackNet-DenVIS: a multi-layer perceptron stacked ensembling approach for COVID-19 detection using X-ray images

Texture Analysis in the Evaluation of Covid-19 Pneumonia in Chest X-Ray Images: a Proof of Concept Study

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2. European radiology

CT Quantification and Machinelearning Models for Assessment of Disease Severity and Prognosis

Detection of coronavirus disease (covid-19) based on deep features

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks

Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks

Development and Validation of a predictive model based on Radiomics topredict the short-term outcomes of patients with COVID-19

CT-based Radiomics Combined with Signs: A Valuable Tool to help Physician Discriminate COVID-19 and Other Viral Pneumonia

Ying-ming Z, Ya-going G. Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics

Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19

Screening of COVID-19 based on the extracted radiomics features from chest CT images

Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert systems with applications

Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features

Novel chest radiographic biomarkers for COVID-19 using radiomic features associated with diagnostics and outcomes

Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis

Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept

A practical integrated radiomics model predicting intensive care hospitalization in COVID-19

A CT radiomics analysis of COVID-19-related ground-glass opacities and consolidation: Is it valuable in a differential diagnosis with other atypical pneumonias?

Deep CNN models for predicting COVID-19 in CT and x-ray images

COVID-19 prediction using AI analytics for South Korea

Development and validation of a prognostic risk score system for COVID-19 inpatients: A multi-center retrospective study in China. Engineering

Quantification of tomographic patterns associated with covid-19 from chest ct

Serial quantitative chest CT assessment of COVID-19: a deep learning approach

Artificial intelligence of COVID-19 imaging: A Hammer in search of a nail

COVID-19, AI enthusiasts, and toy datasets: radiology without radiologists

Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches

Radiologists and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia

Radiomics in medical imaging-"how-to" guide and critical reflection. insights into imaging

Texture analysis of imaging: what radiologists need to know

Viral infection of the lung: host response and sequelae

Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication of Patients with COVID-19 from the Emergency Department

Sensitivity of chest CT for COVID-19: comparison to RT-PCR

This work was supported by the Omani Research Council Grant, grant number RC/COVID-MED/RADI/20/01.

The authors declare that there are no conflicts of interest.

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