Coronaviruses are a large family of viruses that cause disease in humans in the form of a common cold to more severe respiratory infections. An infectious disease caused by a newly discovered coronavirus, also known as COVID-19, is a disease that causes an acute respiratory syndrome, which can lead to the death of infected patients. The disease was first seen in December 2019 in Wuhan, China, and eventually became a global pandemic. According to the official statistics, the number of people infected with the disease had reached over 16 million worldwide, with over 600,000 deaths until end of July 2020 [1].
Patients have a variety of symptoms during the illness, including shortness of breath, fever, dry cough, and chronic fatigue. Sometimes the symptoms are so severe in patients they can be fatal. The leading cause of transmission is the contact of the person's hand with the contaminated surfaces and then touching the face. Despite many efforts by scientists, there is currently no vaccine for the disease. Therefore, the main advice to prevent infection is to observe personal hygiene by regularly washing hands, disinfecting surfaces and covering the airways with a mask [2] .
CT imaging has been proposed as one way to diagnose the disease. A large number of studies have been published on the role of medical imaging in the diagnosis of this disease in the short time since the outbreak stage. Many researchers in medical image analysis are also seeking to provide artificial intelligence (AI) based solution for the automatic diagnosis of the disease based on medical images. This review provides a summary of peer-reviewed research articles, conference papers, case reports and letters to the journal editors related to the role of imaging and also peer-reviewed research articles, conference papers and preprints related to medical image analysis in COVID-19 to help other researchers in conducting their studies due to the importance of this disease and its destructive effects on societies. PubMed, Google Scholar, arXiv and medRxiv were searched for all peer-reviewed journal articles, conference papers, case reports, letters to the journal editors and preprints that fulfil the following selection criteria by the end of April 2020: -the role of all different medical imaging modalities in COVID-19 diagnosis; -imaging findings related to COVID-19; -advice and statements for using imaging in COVID-19 diagnosis; -automated methods for detection and classification COVID-19 based on medical imaging data.
The rest of this review is structured as followed. Section 2 describes the contributions of the role of medical imaging in COVID-19 diagnosis. In this section, many articles, case reports and letters to editors related to this field are reviewed so the reader can understand the main points of these articles can pick up with literature and critical contributions in a short time. In section 3, articles related to automatic methods in the detection of COVID-19 based on AI techniques are reviewed. Finally, in section 4, this article concludes with a discussion and an outlook for future studies.
There are several ways to detect COVID-19, including real-time reverse transcription-polymerase chain reaction (RT-PCR) and the nucleic acid amplification test (NAAT). Because often the test results may be negative despite having the person infected, and asymptomatic infections can spread the infection, there is a need for a more careful approach to diagnosis. Some studies and reports confirm that medical imaging can be an effective way to diagnose COVID-19 infection, even if the patient is asymptomatic. Therefore, where it is impossible to access the above tests, medical imaging can help diagnose the disease and prevent its spread in asymptomatic patients. In this section, we review studies that have been published on the role of medical imaging in the diagnosis of COVID-19. We summarise the most essential points of view of articles, including the features of the disease in medical images.
In the study of Rodrigues et al. [3] , chest imaging findings were examined in the literature. Some pointed out that decisions should be made regarding determining medical imaging as a screening tool in patients with different severity of the disease. In the study of Nair et al. [4] , the use of CT imaging in the diagnosis and management of COVID-19 disease in the UK was investigated by asking several questions and answering them. This study considers the results of CT imaging as a standard in the diagnosis of COVID-19 to be contradictory. As another example, in the study of Huang et al. [5] , it is emphasised that CT imaging should not be recommended as a screening tool in the early diagnosis of COVID-19 due to two issues. The first is failure to prove that CT imaging can always succeed in diagnosing COVID-19, and the second is that overexposure of patients to radiation can have long-term adverse effects. Raptis et al. in their review study [6] concluded that the studies that have examined the role of CT imaging in the diagnosis and management of COVID-19 could not prove this role due to their limitations. Although some studies have debunked the role of medical imaging in the diagnosis of COVID-19, many studies have attempted to prove its importance by expressing the imaging features of the disease, and using CT imaging is considered an effective solution in early diagnosis, severity assessment and patient management in the progression of COVID-19 [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] .
In the study of Zhu et al. [19] , 34 studies with 4121 COVID-19 patients have been systematically reviewed. The main features of CT in this study are ground-glass opacities (GGOs), air bronchogram sign, crazy-paving pattern, consolidation, pleural thickening, lymphadenopathy, and pleural effusion, respectively. Also, Lesion shapes are patchy, spider web sign, cord-like, and nodular. In the study of Li [20] , the importance of using chest CT for the diagnosis and follow-up of COVID-19 patients has been investigated. CT features at different stages of the disease are different according to previous studies. Hani et al. [21] , CT imaging has been cited as a critical complement in the diagnosis of COVID-19. CT features have been described as peripheral GGOs with multifocal distribution, and a progressive evolution towards organizing pneumonia patterns.
In another systematic review study conducted by Bao et al. [22] , 13 studies have been reviewed. CT characteristics have been listed as GGO, GGO with mixed consolidation, adjacent pleura thickening, interlobular septal thickening, air bronchograms, crazy-paving pattern, pleural effusion, bronchiectasis, pericardial effusion, and lymphadenopathy, respectively. The most anatomic distributions are bilateral lung infection and peripheral distribution. In the study by Salehi et al. [23] , 30 studies consisting of 19 case series and 11 case reports with a total of 919 patients were systematically reviewed. CT findings are included GGO, bilateral involvement, peripheral distribution, and multilobar involvement. Other CT findings include interlobular septal thickening, bronchiectasis, pleural thickening, and subpleural involvement. Rarely found findings include pleural effusion, pericardial effusion, lymphadenopathy, cavitation, CT halo sign, and pneumothorax.
CT imaging has been widely used as a fundamental modality in the diagnosis of COVID-19 in the studies. High-resolution [24] [25] [26] [27] [28] [29] , low-dose [30, 31] , thin-section [32, 33] and spiral [34] CT imaging are mentioned as a main modality in some of researches.
Some studies have shown that CT imaging is insufficient or incapable as a diagnostic modality for COVID-19 [35] [36] [37] [38] . Some other studies show misdiagnosis in early-stage patients of COVID-19 [25] , and propose the combination of CT imaging and clinical findings for better diagnosis of COVID-19 [39, 40] , especially in children [41] [42] [43] . A large number of studies have reported the importance of CT imaging in the diagnosis of COVID-19, and CT features related to the patients infected with COVID-19.
GGOs, patchy and wedge-shaped GGOs, consolidation, vascular enlargement and thickening, interlobular septal thickening, interstitial thickening, air bronchogram sign, fibrotic lesions, pleural effusion, crazy-paving pattern, linear and rounded opacities, reticulation, fine reticular opacity, subpleural and central lesions, irregular solid nodules, interstitial pulmonary oedema, halo sign, reversed-halo sign, architectural distortion, bronchial wall thickening, subpleural bands, traction bronchiectasis, intrathoracic lymph node enlargement, lymphadenopathy, thickening of the adjacent pleura, cystic changes, cord-like lesions, thickening of the bronchovascular bundles, pleural thickening, cavitation, tree-in-bud sign, interlobar fissure displacement, pericardial effusion, concomitant hydropericardium and/or hydrothorax, thickened lobular septum, thickened bronchial wall, vacuolar sign, bronchiolar dilatation, secondary tuberculosis, paving stone sign, pleural retraction sign, fine mesh shadow, pneumatocele, spider web sign, enlarged mediastinal nodes, underlying pulmonary emphysema, bullae of lung and obsolete tuberculosis and thickened leaflet interval are CT findings of patients with COVID-19 in the collected studies (Refer to Table 1 ).
Some studies have individually examined the characteristics of CT in children. A study by Li et al. [44] , which was performed on five children, has found patchy GGOs as the main characteristic of CT in children with COVID-19 and believed that the abnormalities in CT images of children are similar but milder than those of adults. The study of Zhu et al. [45] , which was performed on 44 younger (47.5 ± 8.7 y old) and 28 older patients (68.4 ± 6.0 y old) with COVID-19, despite the reporting of some similar CT features among younger and older patients, considers it more likely that extensive lung lobe involvement, subpleural line and pleural thickening will occur in older patients. The study of Feng et al. with 15 cases of paediatric patients diagnosed with COVID-19 [46] , identified small nodular and speckled GGOs as the main features in CT images of these patients. Another study also described CT findings in children as milder than in adults [27] . In a study of nine children with COVID-19 aged 0 to 3 years by Zhou et al. [43] , the CT findings are nodular lesions, patchy lesions, GGO with consolidation and halo sign noted to be milder than in adults. In their study, Liu et al. [41] believe that a history of exposure and clinical symptoms may be more useful in the screening of COVID-19 in children than CT imaging. Procalcitonin elevation and consolidation with surrounding halo signs are frequent in paediatric-patients than adults based on the study of Xia et al. [42] .
Studies have also been performed in pregnant women with COVID-19. According to Wu et al. [31] , the CT findings in pregnant women are similar to those in non-pregnant women. In the study by Liu et al. [41] , consolidation has been described as a more common CT feature in pregnant women. Table 1 is an overview of studies that have examined the role of CT imaging in the diagnosis of COVID-19. These studies have five or more cases of COVID-19, which are sorted based on the number of cases. CT and other related findings are summarised for each study. Table 2 deals with studies on CT imaging for COVID-19 diagnosis with less than five cases mostly written as a case report or letter to the editor. In these tables, to acquaint researchers with the studies conducted in this field, the number of cases, the features of COVID-19 infection in the studied images and the findings or essential points of these studies are summarised.
Among the reviewed studies, seven studies have focused on the signs of COVID-19 in CXR images. In the study of Jacobi et al. [47] , which is a pictorial review, the possibility of using portable CXR imaging in the diagnosis of COVID-19 has been investigated due to its availability in most medical centres. Based on this research, CXR imaging also provides the ability to detect COVID-19. Zhang et al. have reported that the combination of clinical features and radiological findings can predict the severity of COVID-19 [40] . In their study [48] , Wang et al. believe that since the diagnostic role of CT imaging has not been accurately proven, it is better to use a modality such as CXR with less radiation, especially for children.
In the study of Lomoro et al. [49] , which has been performed on 58 patients with COVID-19, CXR manifestations are consolidation and hazy increased opacity. In the study of Wong et al. [50] , conducted with 64 patients with COVID-19, GGOs, consolidation and pleural effusion have been reported as CXR findings. The results of this study show bilateral lower zone consolidation, which peaked at 10-12 days from symptom onset. CXR findings also had sensitivity lower than initial RT-PCR testing. CXR manifestations are parenchymal abnormalities, consolidation, GGOs, single nodular opacity and patchy opacities in the study of Yoon et al. [51] . According to this study, a large proportion of patients with COVID-19 have normal CXR images. Table 3 summarizes the studies related to this section.
Few studies have suggested the use of lung ultrasound to detect COVID-19. In Lu et al. (2020) [52] , lung ultrasound signs are interstitial pulmonary oedema and pulmonary consolidations. The study concluded that although the lung ultrasound diagnostic efficacy on the detection of COVID-19 in mild and moderate patients is relatively low, it is high in severe patients. Thirty patients were examined in this study. In Lomoro et al. (2020) [49] , lung ultrasound signs are B-lines patterns (focal, multifocal, and confluent) due to interlobular septal thickening or hazy opacities, subpleural consolidation, thickened pleural line, pleural effusion and mixed pattern with A-and B-lines based on information from 58 COVID-19 patients.
In the study of Wang et al. [48] , lung ultrasound is considered a suitable modality for the diagnosis of COVID-19 due to its non-radiation nature. Vetrugno et al. [53] also believe that lung ultrasound can show the severity and involvement of the lung in COVID-19. Kalafat et al. (2020) [54] is a case report of positive lung ultrasound findings of COVID-19 in a pregnant woman with an initially negative RT-PCR result.
Some studies have confirmed the role of this modality and its sensitivity to the detection of COVID-19. Based on Lutje et al. (2020) [55] , this modality can play a complementary role in the management of COVID-19. It means this modality has the potential to diagnose COVID-19, and it can be used for estimating the extent to which organs are involved and determining the response to treatment in patients. Deng et al. [56] also highlighted the sensitivity of this modality in COVID-19 diagnosis and monitoring disease progression or the success rate of treatment. In a study by Qin et al. [57] that described the results of 18 F-FDG PET/CT for four patients, all patients had peripheral GGOs and consolidations in over two pulmonary lobes. According to this study, although it is impossible to use this modality widely, it has good potential in detecting the complex cases of COVID-19. Zou and Zhu [58] and Polverari et al. [59] are case reports in which imaging was performed with this modality. The imaging results revealed bilateral, diffuse, and intense FDG uptake in the lower lobes and less intense uptake in the remaining lobes.
However, there are also conflicting points about the effectiveness of this modality. Prolonged imaging may cause the disease to spread in imaging centres [60] . Also, due to the large number of COVID-19 patients, the 18 F-FDG PET/CT imaging capacity cannot cover this number of patients [61] .
Some other studies have talked about other imaging modalities to detect COVID-19. Tulchinsky et al. [62] suggests that since CT-SPECT can diagnose COVID-19, nuclear medicine physicians should be familiar with the features of the disease in the images. The study of Poyiadji et al. [63] is a case report related to using MRI images to detect COVID-19-associated acute necrotising haemorrhagic encephalopathy. Acute necrotising encephalopathy is a rare complication of viral infections like influenza.
When the level of D-dimer increases during hospitalisation or sudden clinical deterioration, CT angiography can be a life-saving option for patients, as patients with COVID-19 may be associated with acute pulmonary embolism [64] .
Due to challenges such as the unavailability of PCR testing in all centres of COVID-19 and the high false-negative rate of this test [65] , which has been mentioned in many studies in this field, medical imaging for early detection of COVID-19 has received more attention. However, evaluating many of medical images in the epidemic situations will undoubtedly be a time-consuming and error-prone process. Therefore, given the advances in machine learning, more reliance on these techniques for the automatic diagnosis of COVID-19 based on medical images should be considered [66] [67] [68] [69] [70] .
AI-based methods can provide automated tools for detecting COVID-19 [69] . The distinguishing features must first be extracted from the image to create automated diagnosis methods. The feature extraction process can either be based on handcrafted feature extraction methods or deep learning approaches [71] . Machine learning approaches can then be used for medical image classification, medical image segmentation, severity assessment of disease and other possible tasks based on extracted features.
This section presents an overview of automated methods in the diagnosis of COVID-19 based on medical imaging. We review the methods, the main contributions of the studies and the imaging datasets available in this field. We also discuss the performance of the methods.
With deep learning, many operations required to analyse images and extract features from images have become more manageable. Convolutional neural networks (CNNs) [72] have been widely used for image analysis, and this review cites several studies that use this approach to detect COVID-19 from CT or CXR images. A look at the famous CNN architectures shows that they all consist of three types of layers. These layers include convolutional, pooling, and fully-connected layers. Convolutional layers, which are based on the use of convolution kernels, are responsible for extracting features from images. Pooling layers reduce the resolution of feature maps based on operations such as average or max-pooling so that they can achieve shift-invariance. Fully-connected layers aim to perform classification based on obtained feature maps from previous layers. The reason for using multiple layers is that the kernels of the first convolutional layer are used to extract the low-level image features such as edges, and the subsequent convolutional layers extract the high-level features of the image. Softmax operation is usually used for the final classification, while other methods such as Support Vector Machine (SVM) can also be used for this purpose [73] .
There is a need for large-scale data to take advantage of deep learning approaches, and some studies have collected data sets required to evaluate the automatic methods of detecting COVID-19 [74, 75] . Big datasets are not yet available for deep learning methods because it has not been long since the COVID-19 pandemic. Therefore, many studies have addressed the challenge of data scarcity using data augmentation [76] or transfer learning [77] .
Transfer learning is a way in which knowledge gained from one domain can learn in another domain. It means it is possible to train a deep neural network and store the knowledge obtained on a domain where there is enough data and that knowledge can be used to train the network with little data from another domain. Two different strategies of transfer learning can be used for image classification. In one strategy, the pre-trained network can be used as a feature extractor, and in another strategy, the pre-trained network can be fine-tuned on images of COVID-19 patients. There are contradictory results regarding the use of these strategies, but in general, using transfer learning dramatically improves the classification accuracy. Using this method can sometimes even outperform human experts [78] .
In the study of Varshni et al. [79] , the use of pre-trained CNNs for feature extraction of CXR images along with different classifiers to distinguish between normal and abnormal images has been investigated for pneumonia detection. Some pre-trained CNN models including Xception [80] , VGG16 and VGG-19 [81] , ResNet-50 [82] , DenseNet-121 and DenseNet-169 [83] , and classifiers including Random Forest (RF), K-nearest neighbours, Naive Bayes and SVM have been evaluated in the study. Based on statistical results, the combination of DenseNet-169 for the feature extraction and SVM for the classification has been selected, and the accuracy of this proposed method is 80%. In the study of Narin et al. [84] , transfer learning has been used on three deep CNNs including ResNet50, InceptionV3 and InceptionResNetV2. A total of 100 CXR images, including 50 images of patients with COVID-19 and 50 normal images, were used to learn the networks, and the results show a 98% accuracy of ResNet50 using 5-fold cross-validation. Another similar study [85] , which used the transfer learning technique to diagnose COVID-19 automatically, evaluates seven deep convolutional neural networks. A 90% accuracy for VGG19 and DenseNet201 in a dataset, including 50 normal images and 25 images of COVID-19 patients, has been achieved. In the study of Ghoshal and Tucker [86] , a pre-trained network called ResNet50V2 has been used. Dropweights based Bayesian CNN (BCNN) has also been used to estimate uncertainty in deep learning strategies to improve diagnostic performance. The study, with 5,941 CXR images, including 68 images of patients with COVID-19, achieved an accuracy of about 89%.
Data augmentation is a way to address the problem of data limitation to avoid network overfitting. Data augmentation can be done with basic image processing techniques or deep learning approaches. The former includes geometric and lightening transformations, image mixing and filtering. Deep learning approaches include generative adversarial learning [76] .
Khalifa et al. [87] have presented a combined approach involving transfer learning and the use of generative adversarial networks (GAN) for data augmentation. A 99% accuracy has been achieved using Resnet18 on a dataset including 5863 CXR images. Loey et al. [88] have used conditional GAN (CGAN) for data augmentation, and this method has improved the performance of classification. In their study, Farooq and Hafeez [89] have used methods such as vertical flips, random rotation of images and lighting condition transformations for data augmentation. Similarly, the study of Apostolopoulos et al. [90] has used random rotation and random horizontal and vertical shift towards any direction for data augmentation. Data augmentation has been done in the study of Zheng et al. [91] using random affine transformation and colour jittering. The affine transformation comprised rotation, horizontal and vertical translations, scaling and shearing in the width dimension. The colour jittering adjusted brightness and contrast. Hu et al. [92] have augmented the data by cropping square patches at the centre of the input frames, rotation with a random angle, random horizontal reflection and contrast adjustment using randomly darkening or brightening.
Some studies have also tried to present a deep neural network architecture from scratch. In a study by Wang et al. [93] , a deep convolutional neural network called COVID-NET is proposed to detect COVID-19 based on 13,975 collected CXR images. This study compared the results of the proposed method with the VGG-19 and ResNet-50, which shows that the accuracy of 93.3% for the proposed method is superior to the other methods. As another example, Oh et al. [94] identified COVID-19 in their study based on a patch-based CNN approach with a relatively small number of trainable parameters. Table 4 summarises the automated deep learning-based approaches for COVID-19 diagnosis.
Some studies use non-deep learning methods to detect COVID-19 automatically. Barstugan et al.'s study [95] used methods such as grey-level co-occurrence matrix (GLCM), local directional pattern (LDP), grey-level run length matrix (GLRLM), grey-level size zone matrix (GLSZM), and discrete wavelet transform (DWT) to extract the feature from 150 CT images. SVM has also been used for classification, and the results show a 99.68% accuracy of the classification using 10-fold cross-validation and GLSZM feature extraction method. In the study of Tang et al. [96] , 63 quantitative features along with a RF model have been used to assess the severity of COVID-19 based on CT images of 176 patients infected with COVID-19. The accuracy of 87.5% has been obtained based on three-fold cross-validation. In the study of Al-Karawi et al. [97] , FFT-Gabor scheme and SVM have been used for feature extraction and classification respectively, with results showing a 95.37% accuracy rate among 275 positive COVID-19, and 195 negative patients.
In this review study, many articles and preprints related to the role of medical imaging and automatic methods of medical image analysis in the diagnosis of COVID-19 were examined. Despite some articles that deny the role of medical imaging in the diagnosis and management of COVID-19, many studies have highlighted this role and examined the characteristics of the disease in medical images. Despite the sometimes contradictory results, a significant portion of the articles emphasises the use of medical images including CT, CXR, ultrasound, 18 F-FDG PET/CT and so on to diagnose COVID-19. The reasons for these studies are that the disease shows visible signs in medical images that can be used for early detection of COVID-19 in the lack of access to RT-PCR and other related methods.
Efforts have also been made to diagnose COVID-19 automatically from CT and CXR images using machine learning techniques. The wide range of machine learning methods, especially deep learning, can be used for COVID-19 diagnosis.
CT is the primary modality in early detection of COVID-19 because a significant portion of the reviewed studies, including 122 studies, examined the role of CT imaging. GGO and consolidation are the most common COVID-19 features in CT images based on the significant number of studies. The study of Ai et al. [98] with 1014 patients studied has the highest population compared to other studies. After that, Zhang et al. [40] and Ling et al. [36] studies are in the next ranks with 645 and 295 patients, respectively. The number of patients in these studies shows there are limitations in terms of patient information. Therefore, more comprehensive studies are needed. There have also been studies on children and pregnant women. Given the conflicting results, more studies are needed in this respect.
However, if we want to summarise, we can still acknowledge the constructive role of medical imaging in the diagnosis of COVID-19.
Due to the growing potential of AI-based approaches for medical diagnosis and interventions, using these approaches to distinguish COVID-19 from medical images has received much attention. With deep learning, the accuracy of the proposed methods has also increased dramatically. In this review study, 51 studies that used deep learning to diagnose COVID-19 were examined. To compare the studies conducted in this field, these studies have been summarised in terms of modality, methodology, accuracy and number of images used in Table 4 . Summarised studies show an accuracy of over 80% in the diagnosis of COVID-19 based on deep learning methods. Therefore, this indicates the ability of deep learning methods in the analysis of medical images.
The main challenge in this section is the lack of big data for more accurate analysis. Although some studies have collected data, it is necessary to collect large dataset in this area due to using deep learning. However, the reviewed studies show the authors used approaches such as transfer learning and data augmentation to overcome this shortcoming. Fine-tuning of pre-trained neural networks for image classification can be an approach to network training with a small number of samples. Data augmentation using GANs or other image processing methods like image rotation and translation, lightening transformations, scaling and so on can also improve the learning and testing process by increasing the number of input images.
Although using deep neural networks has mostly been the basis for most articles in this field, handcrafted feature extraction methods along with classification methods can also be evaluated for the COVID-19 diagnosis.
In this study, two main issues related to COVID-19 were examined. First, the role of medical imaging in the COVID-19 diagnosis was investigated, and the details of the observed characteristics of this disease were listed based on different modalities. Second, AI-based automated methods for COVID-19 diagnosis in various images were reviewed to shed light on the importance of these techniques. This study could be useful for medical staff and technologists who want to get acquainted with the features of COVID-19 in medical images.
In future studies, it is possible to achieve more reliable results by collecting a much broader set of data from different medical centres and relying on approaches that simultaneously use multiple modalities and learning methods. Also, there is a need to develop an international protocol for using medical imaging to diagnose the severity of COVID-19 and its follow-up to control the destructive effects of medical imaging on patients, especially children and pregnant women. GGO and thickening of the interlobular septum in both group. In heart failure group, the ratio of the expansion of small pulmonary veins was also higher.
There are significant differences in chest CT features, such as enlargement of pulmonary veins, lesions distribution and morphology between heart failure and COVID-19. [138] 14 Presence of nodular, GGO, bronchovascular enlarged, irregular linear appearances, consolidation pulmonary opacity and pleural effusion Zhu et al. (2020) [38] 14 GGOs, mixed GGO and consolidation, reticulation, crazy paving, cavitation and bronchiectasis There is a need to develop a new detection technique. Chate et al. (2020) [139] 12
GGOs, crazy-paving pattern, alveolar consolidation, reversed-halo sign and pleural effusion Agostini et al. (2020) [30] 10 GGOs, GGO with consolidation, linear opacities, rounded opacities, crazy-paving pattern, reverse-halo sign, bronchial wall thickening and bronchiectasis
Ultra-low-dose, dual-source, fast CT protocol provides highly diagnostic images for COVID-19 with potential for reduction in dose and motion artefacts. Pure GGO, mixed GGO and consolidation, consolidation, crazy-paving appearance and air bronchogram Iwasawa et al. (2020) [26] 6
GGOs, consolidation, linear opacities, reticulation and crazy-paving pattern U-HRCT can evaluate not only the distribution and hallmarks of COVID-19 pneumonia but also visualise local lung volume loss. Gao and Zhang (2020) [25] 6
GGOs, nodule, halo sign, thickened lobular septum, thickened bronchial wall, tree-in-bud sign, crazy-paving sign, proliferation and calcification
The imaging manifestations of early-stage COVID-19 are relatively mild, and the imaging findings of some patients are not typical, which can easily lead to missed diagnoses. [140] 6 GGO, GGO with consolidation, consolidation, reticulation, crazy paving and bronchiectasis In the early-stage of the disease, the lesion can manifest as round nodular-like GGO in the central area of the lung lobe. The followup CT images showed the lesions are migratory manifested as the absorption of the primary lesions and the emergence of new lesions. Li et al. (2020) [44] 5 Patchy GGOs Similar but more modest lung abnormalities at CT of children compared to adults Liu et al. (2020) [27] 5 GGOs with consolidation The paediatric patients generally have milder CT findings than adults. Lu and Pu (2020) [141] 5
Crazy-paving pattern, GGOs, septal line thickening, consolidation and thickened interlobular septa Xie et al. (2020) [142] 5 Multifocal GGO, parenchyma consolidation, mixed GGO and mixed consolidation Table 2 : Overview of case reports and letters to the editors on CT imaging that have less than five cases of COVID-19.
McGinnis et al. (2020) [143] Asymptomatic COVID-19 was detected using CT imaging in a patient with recurrent non-small cell lung cancer. Yan et al. (2020) [144] Chest CT findings are important when there is a false-positive results for COVID-19. Qi et al. (2020) [145] CT imaging can play an important role in managing patients of COVID-19 for diagnosis and monitoring. Zhang et al. (2020) [146] CT imaging can be helpful for early detection of COVID-19 based on CT findings. Tenda et al. (2020) [147] Three patients with mild to moderate symptoms have been considered. They highly suggest the use of non-contrast chest CT for COVID-19 diagnosis in patients with moderate symptoms. Erturk (2020) [148] CT may help diagnose but not screening highly suspected cases. Xu et al. (2020) [149] Six patients from an extended family with COVID-19 have been investigated. It should be avoided to rely on CT for clinical diagnosis. Liu et al. (2020) [150] Chest CT has an indispensable role in early detection and diagnosis of COVID-19 infection, however, further investigation is needed. Li et al. (2020) [151] Repeated CT scanning could facilitate monitoring disease progression and implementing proper treatment. Ufuk (2020) [152] A patient with peripheral, multilobar areas of GGO sign in chest CT images and positive for COVID-19 has been presented. Hamer et al. (2020) [153] A positive case of COVID-19 and also a review of some studies in this field have been presented. CT morphology can be a support for COVID-19 diagnosis. Kang et al. (2020) [154] A low-dose scanning protocol has been presented that reduces the patient's dose to 1/8 to 1/9 of the standard dose without significant sacrifice of signal-to-noise (SNR) or contrast-to-noise (CNR) ratios. Wang et al. (2020) [48] The role of CT in the diagnosis of COVID-19 is not clear, so it is better to use alternative modalities such as Ultrasound or CXR due to lower radiation, especially for children. Zou and Zhu (2020) [58] A case with GGOs with areas of focal consolidation primarily in the right upper lobe and a focal opacity in the left upper and right middle lobes has been reported. [155] For patients with fever as the first symptom and with a history of exposure to COVID-19, chest CT examination should be performed soon. Lin et al. (2020) [156] Observe changes in CT images during the disease. Lee et al. (2020) [157] More research is needed into the correlation of CT findings with clinical severity and progression of COVID-19. Kim (2020) [158] Role of radiologists includes not only early detection of lung abnormality, but also suggestion of disease severity, potential progression to acute respiratory distress syndrome, and possible bacterial co-infection in hospitalised patients. Zhang et al. (2020) [159] One case of COVID-19 pneumonia showed multiple subpleural GGOs in bilateral lung, rapid progression, and it also accompanied nodular GGOs on chest CT. Singh and Fratesi (2020) [160] CT may expedite care in symptomatic patients with a negative or pending swab, and in those with worsening respiratory status or developing complications such as empyema or acute respiratory distress syndrome. Tsou et al. (2020) [161] In Singapore, the consensus of the infectious diseases experts is to rely on reverse transcriptase polymerase chain reaction (RT-PCR) for diagnosis rather than to use CT. Vu et al. (2020) [162] Patients not initially suspected of COVID-19 infection can be quarantined earlier to limit exposure to others using CT imaging features of COVID-19. Çinkooğlu et al. (2020) [163] Imaging plays a critical role in initial diagnosis and in assessment of disease severity and progression. Asadollahi-Amin et al. (2020) [164] A patient found to be positive COVID-19 after a CT scan performed for an unrelated condition revealed a lesion in the lung field compatible with COVID-19 infection. Chen et al. (2020) [165] Chest CT scan is the primary diagnostic approach for COVID-19 and its feature includes multiple, bilateral, patchy consolidation and GGO with subpleural distribution. [166] Two cases both demonstrated symptom relief but progression on CT, which indicates that clinical symptoms and imaging findings are inconsistent in early-stage of COVID-19 pneumonia. Lim et al. (2020) [167] This case series of three COVID-19 pneumonia patients highlights the variable chest CT features during the acute and convalescent phases. Chest CT is a highly sensitive tool for the delineation of the extent of lung disease. However, its use as a first-line diagnostic modality to replace RT-PCR is not guaranteed. Ostad et al. (2020) [168] Radiologists should know the possibility of artefacts when reporting the axial CT images with limited involvement, especially those cases with focal basal GGO, where linear atelectasis is also common. In such cases correlation with reformatted planes and utilising thin-section reconstructions are recommended to avoid misinterpretation. Qanadli et al. (2020) [169] Vascular findings convey both diagnostic and prognostic information and might contribute to disease diagnosis and patient management. The vascular congestion sign may help distinguish COVID-19 from community-acquired pneumonia. Mungmungpuntipantip and Wiwanitkit (2020) [170] Chest CT cannot discriminate early COVID-19 from other diseases. Danrad et al. (2020) [171] Early and advanced stage CT finding from patients with documented COVID-19 admitted to University medical center in New Orleans, Louisiana have been described. Joob and Wiwanitkit (2020) [172] If patients have underlying lung disease such as tuberculosis, atypical chest CT findings might be seen. Practitioners have to recognise the broad spectrum of possible CT findings in patients with COVID-19. Li et al. (2020) [173] Characteristic imaging changes were found with GGO, consolidation and septal thickening mainly distributed in peripheral and posterior area by thoracic CT scan in the three patients. Guan et al. (2020) [174] A dynamic chest CT scan plays a significant role in the diagnosis and prognosis of COVID-19. Li et al. (2020) [175] CT plays a vital role in the diagnosis, staging, and monitoring of patients with COVID-19 pneumonia. Lei et al. (2020) [176] Knowing the corresponding CT feature of COVID-19 pneumonia at different stages, which could be helpful to precisely diagnose and understand CT characteristics of COVID-19. Gross et al. (2020) [177] CT may be a useful tool to evaluate the extent of the disease in severe cases, provide prognostic information and guide future treatment options. Shi et al. (2020) [178] COVID-19 pneumonia may present with atypical manifestations, such as haemoptysis and focal GGO with non-peripheral distribution, on initial CT scans. Qu et al. (2020) [179] This study is a report of manifestations of COVID-19 in a patient with lung adenocarcinoma. [180] Chest CT offers fast and convenient evaluation of patients with suspected COVID-19 pneumonia. Wei et al. (2020) [181] CT showed rapidly progressing peripheral consolidations and GGOs in both lungs of a 40-year-old female patient with COVID-19 pneumonia. After treatment, the lesions were almost absorbed leaving the fibrous lesions. [182] Under the circumstances, computed tomography imaging is not only useful for the detection, location of lesions but also helpful in evaluating the dynamic changes of patients with COVID-19. CT imaging can play a determinant role in clinical decision-making. Duan and Qin (2020) [183] At seven days, chest CT showed decreasing GGOs in a 46-year-old woman. At day 13 after admission, the GGOs in the right lung had resolved; the left GGOs showed partial resolution. Shi et al. (2020) [184] This study uses imaging data for patient's improvement monitoring in a case with COVID-19. Fang et al. (2020) [185] The authors report two cases of COVID-19 using CT imaging data. Kanne (2020) [186] In the correct clinical setting, bilateral GGOs or consolidation at chest imaging should prompt the radiologist to suggest COVID-19 as a possible diagnosis. A normal chest CT scan does not exclude the diagnosis of COVID-19 infection. Adair and Ledermann (2020) [187] This case report discusses the imaging findings of one of the first cases in the mid-western United States. Burhan et al. (2020) [188] The result may suggest that in an area with high number of COVID-19 case, CT Scan might be a better diagnostic tool compared to RT-PCR in diagnosing COVID-19. Feng et al. (2020) [189] It is challenging to distinguish COVID-19 pneumonia from other viral pneumonia on CT findings alone; however, the authors emphasise the utility of chest CT to detect early change of COVID-19 in cases which RT-PCR tests show negative results. Hao and Li (2020) [190] If patients have clinical symptoms, epidemiological characteristics, and chest CT imaging characteristics of viral pneumonia compatible with COVID-19 infection, we need to carefully consider the isolation and treatment of these patients even if the RT-PCR test is negative. Yang and Yan (2020) [191] A patient with RT-PCR-confirmed COVID-19 infection may have normal chest CT at admission. Lei et al. (2020) [192] The bilateralism of the peripheral lung opacities, without subpleural sparing, are common CT findings of COVID-19 pneumonia. 