Clinical and computed tomographic (CT) images characteristics in the patients with COVID-19 infection: What should radiologists need to know?

A deep transfer learning approach for COVID-19 detection and exploring a sense of belonging with Diabetes

COVID-19 is an epidemic disease that results in death and significantly affects the older adult and those afflicted with chronic medical conditions. Diabetes medication and high blood glucose levels are significant predictors of COVID-19-related death or disease severity. Diabetic individuals, particularly those with preexisting comorbidities or geriatric patients, are at a higher risk of COVID-19 infection, including hospitalization, ICU admission, and death, than those without Diabetes. Everyone's lives have been significantly changed due to the COVID-19 outbreak. Identifying patients infected with COVID-19 in a timely manner is critical to overcoming this challenge. The Real-Time Polymerase Chain Reaction (RT-PCR) diagnostic assay is currently the gold standard for COVID-19 detection. However, RT-PCR is a time-consuming and costly technique requiring a lab kit that is difficult to get in crises and epidemics. This work suggests the CIDICXR-Net50 model, a ResNet-50-based Transfer Learning (TL) method for COVID-19 detection via Chest X-ray (CXR) image classification. The presented model is developed by substituting the final ResNet-50 classifier layer with a new classification head. The model is trained on 3,923 chest X-ray images comprising a substantial dataset of 1,360 viral pneumonia, 1,363 normal, and 1,200 COVID-19 CXR images. The proposed model's performance is evaluated in contrast to the results of six other innovative pre-trained models. The proposed CIDICXR-Net50 model attained 99.11% accuracy on the provided dataset while maintaining 99.15% precision and recall. This study also explores potential relationships between COVID-19 and Diabetes.

Clinical Symptoms and Types of Samples Are Critical Factors for the Molecular Diagnosis of Symptomatic COVID-19 Patients: A Systematic Literature Review

BACKGROUND

Currently, a novel coronavirus found in 2019 known as SARS-CoV-2 is the etiological agent of the COVID-19 pandemic. Various parameters including clinical manifestations and molecular evaluation can affect the accuracy of diagnosis. This review aims to discuss the various clinical symptoms and molecular evaluation results in COVID-19 patients, to point out the importance of onset symptoms, type, and timing of the sampling, besides the methods that are used for detection of SARS-CoV-2.

METHODS

A systematic literature review of current articles in the Web of Science, PubMed, Scopus, and EMBASE was conducted according to the PRISMA guideline.

RESULTS

Of the 12946 patients evaluated in this investigation, 7643 were confirmed to be COVID-19 positive by molecular techniques, particularly the RT-PCR/qPCR combined technique (qRT-PCR). In most of the studies, all of the enrolled cases had 100% positive results for molecular evaluation. Among the COVID-19 patients who were identified as such by positive PCR results, most of them showed fever or cough as the primary clinical signs. Less common symptoms observed in clinically confirmed cases were hemoptysis, bloody sputum, mental disorders, and nasal congestion. The most common clinical samples for PCR-confirmed COVID-19 patients were obtained from throat, oropharyngeal, and nasopharyngeal swabs, while tears and conjunctival secretions seem to be the least common clinical samples for COVID-19 diagnosis among studies. Also, different conserved SARS-CoV-2 gene sequences could be targeted for qRT-PCR detection. The suggested molecular assay being used by most laboratories for the detection of SARS-CoV-2 is qRT-PCR.

CONCLUSION

There is a worldwide concern on the COVID-19 pandemic and a lack of well-managed global control. Hence, it is crucial to update the molecular diagnostics protocols for handling the situation. This is possible by understanding the available advances in assays for the detection of the SARS-CoV-2 infection. Good sampling procedure and using samples with enough viral loads, also considering the onset symptoms, may reduce the qRT-PCR false-negative results in symptomatic COVID-19 patients. Selection of the most efficient primer-probe for target genes and samples containing enough viral loads to search for the existence of SARS-CoV-2 helps detecting the virus on time using qRT-PCR.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

CT scan chest findings in symptomatic COVID-19 patients: a reliable alternative for diagnosis

Computed Tomography (CT) scan of the chest plays an important role in the diagnosis and management of Coronavirus disease 2019 (COVID-19), the disease caused by the novel coronavirus SARS-CoV-2. COVID-19 pneumonia shows typical CT Scan features which can aid diagnoses and therefore help in the early detection and isolation of infected patients. CT scanners are readily available in many parts of Ghana. It is able to show findings typical for COVID-19 infection of the chest, even in instances where Reverse Transcription Polymerase Chain Reaction (RTPCR) misses the diagnosis. Little is known about the diagnostic potential of chest CT scan and COVID-19 among physicians even though CT scan offers a high diagnostic accuracy.

Central nervous system manifestations in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

At the end of December 2019, a novel respiratory infection, initially reported in China, known as COVID-19 initially reported in China, and later known as COVID-19, led to a global pandemic. Despite many studies reporting respiratory infections as the primary manifestations of this illness, an increasing number of investigations have focused on the central nervous system (CNS) manifestations in COVID-19. In this study, we aimed to evaluate the CNS presentations in COVID-19 patients in an attempt to identify the common CNS features and provide a better overview to tackle this new pandemic.

METHODS

In this systematic review and meta-analysis, we searched PubMed, Web of Science, Ovid, EMBASE, Scopus, and Google Scholar. Included studies were publications that reported the CNS features between 1 January 2020 and 20 April 2020. The data of selected studies were screened and extracted independently by four reviewers. Extracted data analyzed by using STATA statistical software. The study protocol registered with PROSPERO (CRD42020184456).

RESULTS

Of 2,353 retrieved studies, we selected 64 studies with 11,687 patients after screening. Most of the studies were conducted in China (58 studies). The most common CNS symptom of COVID-19 was headache (8.69%, 95%CI: 6.76%-10.82%), dizziness (5.94%, 95%CI: 3.66%-8.22%), and impaired consciousness (1.90%, 95%CI: 1.0%-2.79%).

CONCLUSIONS

The growing number of studies has reported COVID-19, CNS presentations as remarkable manifestations that happen. Hence, understanding the CNS characteristics of COVID-19 can help us for better diagnosis and ultimately prevention of worse outcomes.

Mediastinal lymphadenopathy in COVID-19: A review of literature

A novel coronavirus disease 2019 (COVID-19) is a progressive viral disease that affected people around the world with widespread morbidity and mortality. Patients with COVID-19 infection typically had pulmonary manifestation but can also present with gastrointestinal, cardiac, or neurological system dysfunction. Chest imaging in patients with COVID-19 commonly show bilateral lung involvement with bilateral ground-glass opacity and consolidation. Mediastinal lymphadenopathy can be found due to infectious or non-infectious etiologies. It is commonly found to be associated with malignant diseases, sarcoidosis, and heart failure. Mediastinal lymph node enlargement is not a typical computer tomography of the chest finding of patients with COVID-19 infection. We summarized the literature which suggested or investigated the mediastinal lymph node enlargement in patients with COVID-19 infection. Further studies are needed to better characterize the importance of mediastinal lymphadenopathy in patients with COVID-19 infection.

Clinical and CT features of mild-to-moderate COVID-19 cases after two sequential negative nucleic acid testing results: a retrospective analysis

Background

The clinical and imaging features of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections that progressed to coronavirus disease 2019 (COVID-19) have been explored in numerous studies. However, little is known about these features in patients who received negative respiratory nucleic acid test results after the infections resolved. In this study, we aim to describe these features in a group of Chinese patients.

Methods

This retrospective study includes 51 patients with mild-to-moderate COVID-19 (median age: 34.0 years and 47.1% male) between January 31 and February 28, 2020. Demographic, clinical, laboratory, and computed tomography (CT) imaging data were collected before and after two consecutive negative respiratory SARS-CoV-2 tests.

Results

Following a negative test result, the patients’ clinical symptoms continued to recover, but abnormal imaging findings were observed in all moderate cases. Specifically, 77.4% of patients with moderate COVID-19 exhibited multi-lobar lung involvement and lesions were more frequently observed in the lower lobes. The most common CT imaging manifestations were ground-glass opacities (51.6%) and fibrous stripes (54.8%%). Twelve of the 31 patients with moderate COVID-19 underwent repeated chest CT scans after a negative SARS-CoV-2 test. Among them, the ground-glass opacities decreased by > 60% within 1 week in seven patients (58.3%), but by < 5% in four patients (13.8%).

Conclusions

Following a positive and subsequent negative SARS-CoV-2 tests, patients with COVID-19 continued to recover despite exhibiting persistent clinical symptoms and abnormal imaging findings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-06013-x.

Developing a Training Web Application for Improving the COVID-19 Diagnostic Accuracy on Chest X-ray

In December 2019, a new coronavirus known as 2019-nCoV emerged in Wuhan, China. The virus has spread globally and the infection was declared pandemic in March 2020. Although most cases of coronavirus disease 2019 (COVID-19) are mild, some of them rapidly develop acute respiratory distress syndrome. In the clinical management, chest X-rays (CXR) are essential, but the evaluation of COVID-19 CXR could be a challenge. In this context, we developed COVID-19 TRAINING, a free Web application for training on the evaluation of COVID-19 CXR. The application included 196 CXR belonging to three categories: non-pathological, pathological compatible with COVID-19, and pathological non-compatible with COVID-19. On the training screen, images were shown to the users and they chose a diagnosis among those three possibilities. At any time, users could finish the training session and be evaluated through the estimation of their diagnostic accuracy values: sensitivity, specificity, predictive values, and global accuracy. Images were hand-labeled by four thoracic radiologists. Average values for sensitivity, specificity, and global accuracy were .72, .64, and .68. Users who achieved better sensitivity registered less specificity (p < .0001) and those with higher specificity decreased their sensitivity (p < .0001). Users who sent more answers achieved better accuracy (p = .0002). The application COVID-19 TRAINING provides a revolutionary tool to learn the necessary skills to evaluate COVID-19 on CXR. Diagnosis training applications could provide a new original manner of evaluation for medical professionals based on their diagnostic accuracy values, and an efficient method to collect valuable data for research purposes.

XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks

COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.

Computer aid screening of COVID-19 using X-ray and CT scan images: An inner comparison

The objective of this study is to conduct a critical analysis to investigate and compare a group of computer aid screening methods of COVID-19 using chest X-ray images and computed tomography (CT) images. The computer aid screening method includes deep feature extraction, transfer learning, and machine learning image classification approach. The deep feature extraction and transfer learning method considered 13 pre-trained CNN models. The machine learning approach includes three sets of handcrafted features and three classifiers. The pre-trained CNN models include AlexNet, GoogleNet, VGG16, VGG19, Densenet201, Resnet18, Resnet50, Resnet101, Inceptionv3, Inceptionresnetv2, Xception, MobileNetv2 and ShuffleNet. The handcrafted features are GLCM, LBP & HOG, and machine learning based classifiers are KNN, SVM & Naive Bayes. In addition, the different paradigms of classifiers are also analyzed. Overall, the comparative analysis is carried out in 65 classification models, i.e., 13 in deep feature extraction, 13 in transfer learning, and 39 in the machine learning approaches. Finally, all classification models perform better when applying to the chest X-ray image set as comparing to the use of CT scan image set. Among 65 classification models, the VGG19 with SVM achieved the highest accuracy of 99.81%when applying to the chest X-ray images. In conclusion, the findings of this analysis study are beneficial for the researchers who are working towards designing computer aid tools for screening COVID-19 infection diseases.

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections

BACKGROUND

Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.

PURPOSE

In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans.

METHODS

For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infection cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance.

RESULTS

Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists' performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance.

CONCLUSION

A deep learning algorithm-based AI model developed in this study successfully improved radiologists' performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

Nowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords "COVID19 virus", "2019 novel coronavirus", "Wuhan coronavirus", "2019-nCoV", "X-Ray Computed Tomography", "Polymerase Chain Reaction", "Reverse Transcriptase PCR", and "PCR Reverse Transcriptase". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85-90%), 46% (95% CI 29-63%), 69% (95% CI 56-72%), and 89% (95% CI 82-96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.

Chest Computed Tomography findings in patients with corona virus disease 2019 (COVID-19): An initial experience in three centres in Ghana, West Africa

BACKGROUND

Radiological examinations have a significant role in the diagnosis and management of Coronavirus disease 2019 (COVID-19), the disease caused by the novel coronavirus SARS-CoV-2. Many COVID-19 patients show typical Chest Computed Tomography (CT Scan) features which can aid in the diagnoses and triaging of such patients. This is especially so in resource-limited settings where access to molecular diagnostic techniques such as Reverse Transcription Polymerase Chain Reaction (RT-PCR) is not optimal. We report chest CT findings in 28 patients diagnosed with COVID-19 in Ghana.

OBJECTIVE

To document common chest CT scan findings amongst patients with COVID-19 infection in Ghana.

METHOD

Chest CT scans of twenty-eight COVID-19 patients (n = 28) were retrieved and reviewed independently by two experienced radiologists and their findings documented. Two 64 and one 32 slice spiral CT scanners were used at three centres.

RESULTS

Chest CT Images from 16 males (57.1.7%) and 12 females (42.9%) patients aged between 36 and 65 years with mean age of 55.9 years (SD-8.4years) were evaluated. Of these, 21 (75.0%) of them were COVID-19 patients who were undiagnosed at the time of imaging while 7 (25.0%) were known confirmed COVID-19 patients. On the chest CT scans (n = 28), 17 (66.7%) patients showed predominantly ground glass opacities while 12 (42.9%) had evidence of consolidation predominantly. In 26 (92.9%) of the patients, the opacities were bilateral and peripheral in distribution. None of these patients had pleural effusion.

CONCLUSION

COVID-19 patients tend to manifest typical imaging features on chest CT scan. The most common chest imaging finding was bilateral, peripheral and predominantly basal ground glass opacities. Importantly, these findings were frequently obtained before PCR diagnosis. Chest CT scan can help in the diagnosis and triaging of suspected or confirmed COVID-19 patients in jurisdictions with limited PCR diagnostic capacity and can improve early isolation, contact tracing and treatment thus helping to reduce community spread, morbidity and mortality.

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms

OBJECTIVE

This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

METHOD

CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. To build and test the CNN model, a publicly available dataset involving 8474 chest X-ray images is used, which includes 415, 5179 and 2,880 cases in three classes, respectively. Dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class to train and test the CNN model.

RESULTS

The CNN-based CAD scheme yields an overall accuracy of 94.5 % (2404/2544) with a 95 % confidence interval of [0.93,0.96] in classifying 3 classes. CAD also yields 98.4 % sensitivity (124/126) and 98.0 % specificity (2371/2418) in classifying cases with and without COVID-19 infection. However, without using two preprocessing steps, CAD yields a lower classification accuracy of 88.0 % (2239/2544).

CONCLUSION

This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.

Global prevalence and pathogenesis of headache in COVID-19: A systematic review and meta-analysis

Background: This study was conducted to determine the prevalence of headache in coronavirus disease 2019 (COVID-19) and to assess its association as a predictor for COVID-19. This study also aimed to discuss the possible pathogenesis of headache in COVID-19. Methods: Available articles from PubMed, Scopus, and Web of Science were searched as of September 2 nd, 2020. Data on characteristics of the study, headache and COVID-19 were extracted following the PRISMA guidelines. Biases were assessed using the Newcastle-Ottawa scale. The cumulative prevalence of headache was calculated for the general population (i.e. adults and children). The pooled odd ratio (OR) with 95% confidence intervals (95%CI) was calculated using the Z test to assess the association between headache and the presence of COVID-19 cases. Results: We included 104,751 COVID-19 cases from 78 eligible studies to calculate the global prevalence of headache in COVID-19 and 17 studies were included to calculate the association of headache and COVID-19. The cumulative prevalence of headache in COVID-19 was 25.2% (26,464 out of 104,751 cases). Headache was found to be more prevalent, approximately by two-fold, in COVID-19 patients than in non-COVID-19 patients with symptoms of other respiratory viral infections, OR: 1.73; 95% CI: 1.94, 2.5 with p=0.04. Conclusion: Headache is common among COVID-19 patients and seems to be more common in COVID-19 patients compared to those with the non-COVID-19 viral infection. No definitive mechanisms on how headache  emerges in COVID-19 patients but several possible hypotheses have been proposed. However, extensive studies are warranted to elucidate the mechanisms. PROSPERO registration: CRD42020210332 (28/09/2020).

Global prevalence and pathogenesis of headache in COVID-19: A systematic review and meta-analysis

Background: This study was conducted to determine the prevalence of headache in coronavirus disease 2019 (COVID-19) and to assess its association as a predictor for COVID-19. This study also aimed to discuss the possible pathogenesis of headache in COVID-19. Methods: Available articles from PubMed, Scopus, and Web of Science were searched as of September 2 nd, 2020. Data on characteristics of the study, headache and COVID-19 were extracted following the PRISMA guidelines. Biases were assessed using the Newcastle-Ottawa scale. The cumulative prevalence of headache was calculated for the general population (i.e. adults and children). The pooled odd ratio (OR) with 95% confidence intervals (95%CI) was calculated using the Z test to assess the association between headache and the presence of COVID-19 cases. Results: We included 104,751 COVID-19 cases from 78 eligible studies to calculate the global prevalence of headache in COVID-19 and 17 studies were included to calculate the association of headache and COVID-19. The cumulative prevalence of headache in COVID-19 was 25.2% (26,464 out of 104,751 cases). Headache was found to be more prevalent, approximately by two-fold, in COVID-19 patients than in non-COVID-19 patients (other respiratory viral infections), OR: 1.73; 95% CI: 1.94, 2.5 with p=0.04. Conclusion: Headache is common among COVID-19 patients and seems to be more common in COVID-19 patients compared to those with the non-COVID-19 viral infection. No definitive mechanisms on how headache  emerges in COVID-19 patients but several possible hypotheses have been proposed. However, extensive studies are warranted to elucidate the mechanisms. PROSPERO registration: CRD42020210332 (28/09/2020).

Thoracic imaging tests for the diagnosis of COVID-19

BACKGROUND

The diagnosis of infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents major challenges. Reverse transcriptase polymerase chain reaction (RT-PCR) testing is used to diagnose a current infection, but its utility as a reference standard is constrained by sampling errors, limited sensitivity (71% to 98%), and dependence on the timing of specimen collection. Chest imaging tests are being used in the diagnosis of COVID-19 disease, or when RT-PCR testing is unavailable.

OBJECTIVES

To determine the diagnostic accuracy of chest imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected or confirmed COVID-19.

SEARCH METHODS

We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, and The Stephen B. Thacker CDC Library. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 5 May 2020.

SELECTION CRITERIA

We included studies of all designs that produce estimates of test accuracy or provide data from which estimates can be computed. We included two types of cross-sectional designs: a) where all patients suspected of the target condition enter the study through the same route and b) where it is not clear up front who has and who does not have the target condition, or where the patients with the target condition are recruited in a different way or from a different population from the patients without the target condition. When studies used a variety of reference standards, we included all of them.

DATA COLLECTION AND ANALYSIS

We screened studies and extracted data independently, in duplicate. We also assessed the risk of bias and applicability concerns independently, in duplicate, using the QUADAS-2 checklist and presented the results of estimated sensitivity and specificity, using paired forest plots, and summarised in tables. We used a hierarchical meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs).

MAIN RESULTS

We included 84 studies, falling into two categories: studies with participants with confirmed diagnoses of COVID-19 at the time of recruitment (71 studies with 6331 participants) and studies with participants suspected of COVID-19 (13 studies with 1948 participants, including three case-control studies with 549 cases and controls). Chest CT was evaluated in 78 studies (8105 participants), chest X-ray in nine studies (682 COVID-19 cases), and chest ultrasound in two studies (32 COVID-19 cases). All evaluations of chest X-ray and ultrasound were conducted in studies with confirmed diagnoses only. Twenty-five per cent (21/84) of all studies were available only as preprints, 15/71 studies in the confirmed cases group and 6/13 of the studies in the suspected group. Among 71 studies that included confirmed cases, 41 studies had included symptomatic cases only, 25 studies had included cases regardless of their symptoms, five studies had included asymptomatic cases only, three of which included a combination of confirmed and suspected cases. Seventy studies were conducted in Asia, 2 in Europe, 2 in North America and one in South America. Fifty-one studies included inpatients while the remaining 24 studies were conducted in mixed or unclear settings. Risk of bias was high in most studies, mainly due to concerns about selection of participants and applicability. Among the 13 studies that included suspected cases, nine studies were conducted in Asia, and one in Europe. Seven studies included inpatients while the remaining three studies were conducted in mixed or unclear settings. In studies that included confirmed cases the pooled sensitivity of chest CT was 93.1% (95%CI: 90.2 - 95.0 (65 studies, 5759 cases); and for X-ray 82.1% (95%CI: 62.5 to 92.7 (9 studies, 682 cases). Heterogeneity judged by visual assessment of the ROC plots was considerable. Two studies evaluated the diagnostic accuracy of point-of-care ultrasound and both reported zero false negatives (with 10 and 22 participants having undergone ultrasound, respectively). These studies only reported True Positive and False Negative data, therefore it was not possible to pool and derive estimates of specificity. In studies that included suspected cases, the pooled sensitivity of CT was 86.2% (95%CI: 71.9 to 93.8 (13 studies, 2346 participants) and specificity was 18.1% (95%CI: 3.71 to 55.8). Heterogeneity judged by visual assessment of the forest plots was high. Chest CT may give approximately the same proportion of positive results for patients with and without a SARS-CoV-2 infection: the chances of getting a positive CT result are 86% (95% CI: 72 to 94) in patient with a SARS-CoV-2 infection and 82% (95% CI: 44 to 96) in patients without.

AUTHORS' CONCLUSIONS

The uncertainty resulting from the poor study quality and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results. Our findings indicate that chest CT is sensitive but not specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may not be capable of differentiating SARS-CoV-2 infection from other causes of respiratory illness. This low specificity could also be the result of the poor sensitivity of the reference standard (RT-PCR), as CT could potentially be more sensitive than RT-PCR in some cases. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of COVID-19 should be carefully interpreted. Future diagnostic accuracy studies should avoid cases-only studies and pre-define positive imaging findings. Planned updates of this review will aim to: increase precision around the accuracy estimates for CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X rays and ultrasound; and continue to search for studies that fulfil secondary objectives to inform the utility of imaging along different diagnostic pathways.

Coronavirus disease 2019 (COVID-19) complicated by Spontaneous Pneumomediastinum and Pneumothorax

The first reports of severe acute respiratory symptoms from a novel coronavirus called coronavirus disease 2019 (COVID-19) occurred in Wuhan, Hubei Province, China in December 2019.¹ The World Health Organization declared COVID-19 a global pandemic by March 2020.¹ The COVID-19 outbreak has resulted in a current global health emergency. Clinical information about the findings of COVID-19 and its associated complications are constantly evolving and becoming more widely available. Providers should be familiar with both typical symptoms and image study results for COVID-19 as well as less commonly reported complications of progressive COVID-19, such as spontaneous pneumomediastinum and spontaneous pneumothorax as highlighted in this case.

Systematic review with meta-analysis: liver manifestations and outcomes in COVID-19

BACKGROUND

The incidence of elevated liver chemistries and the presence of pre-existing chronic liver disease (CLD) have been variably reported in COVID-19.

AIMS

To assess the prevalence of CLD, the incidence of elevated liver chemistries and the outcomes of patients with and without underlying CLD/elevated liver chemistries in COVID-19.

METHODS

A comprehensive search of electronic databases from 1 December 2019 to 24 April 2020 was done. We included studies reporting underlying CLD or elevated liver chemistries and patient outcomes in COVID-19.

RESULTS

107 articles (n = 20 874 patients) were included for the systematic review. The pooled prevalence of underlying CLD was 3.6% (95% CI, 2.5-5.1) among the 15 407 COVID-19 patients. The pooled incidence of elevated liver chemistries in COVID-19 was 23.1% (19.3-27.3) at initial presentation. Additionally, 24.4% (13.5-40) developed elevated liver chemistries during the illness. The pooled incidence of drug-induced liver injury was 25.4% (14.2-41.4). The pooled prevalence of CLD among 1587 severely infected patients was 3.9% (3%-5.2%). The odds of developing severe COVID-19 in CLD patients was 0.81 (0.31-2.09; P = 0.67) compared to non-CLD patients. COVID-19 patients with elevated liver chemistries had increased risk of mortality (OR-3.46 [2.42-4.95, P < 0.001]) and severe disease (OR-2.87 [95% CI, 2.29-3.6, P < 0.001]) compared to patients without elevated liver chemistries.

CONCLUSIONS

Elevated liver chemistries are common at presentation and during COVID-19. The severity of elevated liver chemistries correlates with the outcome of COVID-19. The presence of CLD does not alter the outcome of COVID-19. Further studies are needed to analyse the outcomes of compensated and decompensated liver disease.

Pulmonary vascular enlargement on thoracic CT for diagnosis and differential diagnosis of COVID-19: a systematic review and meta-analysis

Background

The 2019 coronavirus disease (COVID-19) has become a global pandemic. To date, although many studies have reported on the computed tomography (CT) manifestations of COVID-19, the vascular enlargement sign (VES) of COVID-19 has not been deeply examined, with the few available studies reporting an inconsistent prevalence. We thus performed a systematic review and meta-analysis based on the best available studies to estimate the prevalence and identify the underlying differential diagnostic value of VES.

Methods

We searched nine English and Chinese language databases up to April 23, 2020. Studies that evaluated CT features of COVID-19 patients and reported VES, with or without comparison with other pneumonia were included. The methodologic quality was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Meta-analyses with random effects models were performed to calculate the aggregate prevalence and pooled odds ratios (ORs) of VES. We also conducted meta-regression and subgroup analyses to analyze heterogeneity.

Results

VES findings from a total of 1969 patients were summarized and pooled across 22 studies. Our analysis demonstrated that the prevalence of VES among COVID-19 patients was 69.37% [95% confidence interval (CI): 57.40-79.20%]. Compared with non-COVID-19 patients, VES manifestation was more frequently observed in confirmed COVID-19 patients (OR =6.43, 95% CI: 3.39-12.22). Studies that explicitly defined distribution of VES in the lesion area demonstrated a significantly higher prevalence (P=0.03). Subgroup analyses also revealed a relatively higher VES rate in studies with a sample size larger than 50, but the difference was not statistically significant. No significant difference in VES rates was found between different countries (China/Italy), regions (Hubei/outside Hubei), average age groups (over/less than 50-year-old), or slice thicknesses of CT scan. Extensive heterogeneity was identified across most estimates (I²>80%). Some of the variations (R²=19.73%) could be explained by VES distribution, and sample size. No significant publication bias was seen (P=0.29).

Conclusions

VES on thoracic CT was found in almost two-thirds of COVID-19 patients, and was more prevalent compared with that of the non-COVID-19 patients, supporting a promising role for VES in identifying pneumonia caused by coronavirus.

Neurological and Musculoskeletal Features of COVID-19: A Systematic Review and Meta-Analysis

Importance: Some of the symptoms of COVID-19 are fever, cough, and breathing difficulty. However, the mechanism of the disease, including some of the symptoms such as the neurological and musculoskeletal symptoms, is still poorly understood. Objective: The aim of this review is to summarize the evidence on the neurological and musculoskeletal symptoms of the disease. This may help with early diagnosis, prevention of disease spread, and treatment planning. Data Sources: MEDLINE, EMBASE, Web of Science, and Google Scholar (first 100 hits) were searched until April 17, 2020. The key search terms used were "coronavirus" and "signs and symptoms." Only studies written in English were included. Study Selection: The selection was performed by two independent reviewers using EndNote and Rayyan software. Any disagreement was resolved by consensus or by a third reviewer. Data Extraction and Synthesis: PRISMA guidelines were followed for abstracting data and assessing the quality of the studies. These were carried out by two and three independent reviewers, respectively. Any disagreement was resolved by consensus or by a third reviewer. The data were analyzed using qualitative synthesis and pooled using a random-effect model. Main Outcome(s) and Measure(s): The outcomes in the study include country, study design, participant details (sex, age, sample size), and neurological and musculoskeletal features. Result: Sixty studies (n = 11, 069) were included in the review, and 51 studies were used in the meta-analysis. The median or mean age ranged from 24 to 95 years. The prevalence of neurological and musculoskeletal manifestations was 35% for smell impairment (95% CI 0-94%; I 2 99.63%), 33% for taste impairment (95% CI 0-91%; I 2 99.58%), 19% for myalgia (95% CI 16-23; I 2 95%), 12% for headache (95% CI 9-15; I 2 93.12%), 10% for back pain (95% CI 1-23%; I 2 80.20%), 10% for dizziness (95% CI 3-19%; I 2 86.74%), 3% for acute cerebrovascular disease (95% CI 1-5%; I 2 0%), and 2% for impaired consciousness (95% CI 1-2%; I 2 0%). Conclusion and Relevance: Patients with COVID-19 present with neurological and musculoskeletal symptoms. Therefore, clinicians need to be vigilant in the diagnosis and treatment of these patients.

Evaluation of dynamic lung changes during coronavirus disease 2019 (COVID-19) by quantitative computed tomography

OBJECTIVES

This study aims to trace the dynamic lung changes of coronavirus disease 2019 (COVID-19) using computed tomography (CT) images by a quantitative method.

METHODS

In this retrospective study, 28 confirmed COVID-19 cases with 145 CT scans are collected. The lesions are detected automatically and the parameters including lesion volume (LeV/mL), lesion percentage to lung volume (LeV%), mean lesion density (MLeD/HU), low attenuation area lower than - 400HU (LAA-400%), and lesion weight (LM/mL*HU) are computed for quantification. The dynamic changes of lungs are traced from the day of initial symptoms to the day of discharge. The lesion distribution among the five lobes and the dynamic changes in each lobe are also analyzed.

RESULTS

LeV%, MLeD, and LM reach peaks on days 9, 6 and 8, followed by a decrease trend in the next two weeks. LAA-400% (mostly the ground glass opacity) declines to the lowest on days 4-5, and then increases. The lesion is mostly seen in the bilateral lower lobes, followed by the left upper lobe, right upper lobe and right middle lobe (p < 0.05). The right middle lobe is the earliest one (on days 6-7), while the right lower lobe is the latest one (on days 9-10) that reaches to peak among the five lobes.

CONCLUSIONS

Severity of COVID-19 increases from the day of initial symptoms, reaches to the peak around on day 8, and then decreases. Lesion is more commonly seen in the bilateral lower lobes.

A retrospective study of the initial chest CT imaging findings in 50 COVID-19 patients stratified by gender and age

OBJECTIVE

To retrospectively analyze and stratify the initial clinical features and chest CT imaging findings of patients with COVID-19 by gender and age.

METHODS

Data of 50 COVID-19 patients were collected in two hospitals. The clinical manifestations, laboratory examination and chest CT imaging features were analyzed, and a stratification analysis was performed according to gender and age [younger group: <50 years old, elderly group ≥50 years old].

RESULTS

Most patients had a history of epidemic exposure within 2 weeks (96%). The main clinical complaints are fever (54%) and cough (46%). In chest CT images, ground-glass opacity (GGO) is the most common feature (37/38, 97%) in abnormal CT findings, with the remaining 12 patients (12/50, 24%) presenting normal CT images. Other concomitant abnormalities include dilatation of vessels in lesion (76%), interlobular thickening (47%), adjacent pleural thickening (37%), focal consolidation (26%), nodules (16%) and honeycomb pattern (13%). The lesions were distributed in the periphery (50%) or mixed (50%). Subgroup analysis showed that there was no difference in the gender distribution of all the clinical and imaging features. Laboratory findings, interlobular thickening, honeycomb pattern and nodules demonstrated remarkable difference between younger group and elderly group. The average CT score for pulmonary involvement degree was 5.0±4.7. Correlation analysis revealed that CT score was significantly correlated with age, body temperature and days from illness onset (p < 0.05).

CONCLUSIONS

COVID-19 has various clinical and imaging appearances. However, it has certain characteristics that can be stratified. CT plays an important role in disease diagnosis and early intervention.

Detection of coronavirus disease from X-ray images using deep learning and transfer learning algorithms

OBJECTIVE

This study aims to employ the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of novel coronavirus (COVID-19) infected disease.

METHOD

This study applied transfer learning method to develop deep learning models for detecting COVID-19 disease. Three existing state-of-the-art deep learning models namely, Inception ResNetV2, InceptionNetV3 and NASNetLarge, were selected and fine-tuned to automatically detect and diagnose COVID-19 disease using chest X-ray images. A dataset involving 850 images with the confirmed COVID-19 disease, 500 images of community-acquired (non-COVID-19) pneumonia cases and 915 normal chest X-ray images was used in this study.

RESULTS

Among the three models, InceptionNetV3 yielded the best performance with accuracy levels of 98.63% and 99.02% with and without using data augmentation in model training, respectively. All the performed networks tend to overfitting (with high training accuracy) when data augmentation is not used, this is due to the limited amount of image data used for training and validation.

CONCLUSION

This study demonstrated that a deep transfer learning is feasible to detect COVID-19 disease automatically from chest X-ray by training the learning model with chest X-ray images mixed with COVID-19 patients, other pneumonia affected patients and people with healthy lungs, which may help doctors more effectively make their clinical decisions. The study also gives an insight to how transfer learning was used to automatically detect the COVID-19 disease. In future studies, as the amount of available dataset increases, different convolution neutral network models could be designed to achieve the goal more efficiently.

Differentiating pneumonia with and without COVID-19 using chest CT images: from qualitative to quantitative

BACKGROUND

Pneumonia caused by COVID-19 shares overlapping imaging manifestations with other types of pneumonia. How to objectively and quantitatively differentiate pneumonia patients with and without COVID-19 virus remains clinical challenge.

OBJECTIVE

To formulate standardized scoring criteria and an objective quantization standard to guide decision making in detection and diagnosis of COVID-19 virus induced pneumonia in clinical practice.

METHODS

A retrospective dataset includes computed tomography (CT) images acquired from 43 pneumonia patients with COVID-19 virus detected by reverse transcription-polymerase chain reaction (RT-PCR) tests and 49 pneumonia patients without COVID-19 virus. All patients were treated during the same time period in two hospitals. Key indicators of differential diagnosis were identified in relevant literature and the scores were quantified namely, patients with more than 8 points were identified as high risk, those with 6-8 points as moderate risk, and those with fewer than 6 points as low risk for COVID-19 virus. In the study, 3 radiologists determined the scores for all patients. Diagnostic sensitivity and specificity were subsequently calculated.

RESULTS

A total of 61 patients were determined as high risk, among which 42 were COVID-19 positive by RT-PCR tests. Next, 9 were identified as moderate risk, one of whom was COVID-19 positive. Last, 22 were classified into the low-risk group, all of them are COVID-19 negative. Based on these results, the sensitivity of detection COVID-19 positive cases between the high-risk group and the non-high-risk group was 0.98 with 95% confidence interval [0.88, 1.00], and the specificity was 0.61 [0.46, 0.75]. The detection sensitivity between the moderate-/high-risk group and the low-risk group was 1.00 [0.92, 1.00], and the specificity was 0.45 [0.31, 0.60].

CONCLUSION

The proposed quantitative scoring criteria showed high sensitivity and moderate specificity in detecting COVID-19 using CT images, which indicates that these criteria may be beneficial for screening in real-world practice and helpful for long-term disease control.