The Many Faces of COVID-19: Spectrum of Imaging Manifestations

Diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on duration of symptoms

Background

Chest computed tomography (CT) imaging provides results more rapidly and with higher sensitivity than reverse transcription polymerase chain reaction in diagnosis of COVID-19.

Aim

To evaluate diagnostic efficacy of chest CT imaging in diagnosis of COVID-19 cases based on age and duration of symptoms.

Materials and Methods

A retrospective study conducted during December 2020 to June 2021 in a tertiary care hospital, India. Total 495 patients with typical clinical symptoms of COVID-19, reverse transcription polymerase chain reaction positive for COVID-19 and had undergone chest CT imaging were included. Descriptive statistical analysis was performed for all the variables. Receiver operating characteristic curve analysis was used to determine threshold value of chest CT severity score (CT_SS) based on duration of symptoms and age to diagnose COVID-19.

Results

Mean age of patients was 61.86 ± 10.77 years and 367 (71.4%) patients were male. Ground glass opacities were observed in 456 (92.1%) patients and in 332 (67.1%) patients, multilobes were affected. Total CT_SS showed positive correlation with age (r = 0.257) and duration of symptoms (r = 0.625). Total CT_SS >6 after a duration of 2 days of symptoms identified COVID-19 cases with sensitivity 90.8% (95% confidence interval [CI]: 87.5%-93.5%) and specificity 84.6% (95% CI: 76.2%-90.9%). Total CT_SS >11 in patients aged more than 60 years identified COVID-19 cases with sensitivity 47.4% (95% CI: 41.2%-53.6%) and specificity 87.3% (95% CI: 82.3%-91.4%).

Conclusion

Threshold value of CT_SS determined will help to expedite diagnosis of COVID-19 patients by the clinicians in an early stage especially in India and other developing countries which have a high patient volume and limited health resources.

Association between emphysema and other pulmonary computed tomography patterns in COVID-19 pneumonia

To evaluate the chest computed tomography (CT) findings of patients with Corona Virus Disease 2019 (COVID-19) on admission to hospital. And then correlate CT pulmonary infiltrates involvement with the findings of emphysema. We analyzed the different infiltrates of COVID-19 pneumonia using emphysema as the grade of pneumonia. We applied open-source assisted software (3D Slicer) to model the lungs and lesions of 66 patients with COVID-19, which were retrospectively included. we divided the 66 COVID-19 patients into the following two groups: (A) 12 patients with less than 10% emphysema in the low-attenuation area less than -950 Hounsfield units (%LAA-950), (B) 54 patients with greater than or equal to 10% emphysema in %LAA-950. Imaging findings were assessed retrospectively by two authors and then pulmonary infiltrates and emphysema volumes were measured on CT using 3D Slicer software. Differences between pulmonary infiltrates, emphysema, Collapsed, affected of patients with CT findings were assessed by Kruskal-Wallis and Wilcoxon test, respectively. Statistical significance was set at p < 0.05. The left lung (A) affected left lung 20.00/affected right lung 18.50, (B) affected left lung 13.00/affected right lung 11.50 was most frequently involved region in COVID-19. In addition, collapsed left lung, (A) collapsed left lung 4.95/collapsed right lung 4.65, (B) collapsed left lung 3.65/collapsed right lung 3.15 was also more severe than the right one. There were significant differences between the Group A and Group B in terms of the percentage of CT involvement in each lung region (p < 0.05), except for the inflated affected total lung (p = 0.152). The median percentage of collapsed left lung in the Group A was 20.00 (14.00-30.00), right lung was 18.50 (13.00-30.25) and the total was 19.00 (13.00-30.00), while the median percentage of collapsed left lung in the Group B was 13.00 (10.00-14.75), right lung was 11.50 (10.00-15.00) and the total was 12.50 (10.00-15.00). The percentage of affected left lung is an independent predictor of emphysema in COVID-19 patients. We need to focus on the left lung of the patient as it is more affected. The people with lower levels of emphysema may have more collapsed segments. The more collapsed segments may lead to more serious clinical feature.

Revisiting Middle East Respiratory Coronavirus (MERS-CoV) Outbreak Chest Radiographic Initial Findings, Temporal Progression, and Correlation to Outcomes: A Multicenter Study

Objectives

Accounts of initial and follow-up chest X-rays (CXRs) of the Middle East respiratory coronavirus (MERS-CoV) patients, and correlation with outcomes, are sparse. We retrospectively evaluated MERS-CoV CXRs initial findings, temporal progression, and outcomes correlation.

Materials and methods

Fifty-three real-time reverse-transcriptase-polymerase chain reaction (rRT-PCR)-confirmed MERS-CoV patients with CXRs were retrospectively identified from November 2013 to October 2014. Initial and follow-up CXR imaging findings and distribution were evaluated over 75 days. Findings were correlated with outcomes.

Results

Twenty-two of 53 (42%) initial CXRs were normal. In 31 (68%) abnormal initial CXRs, 15 (48%) showed bilateral non-diffuse involvement, 16 (52%) had ground-glass opacities (GGO), and 13 (42%) had peripheral distribution. On follow-up CXRs, mixed airspace opacities prevailed, seen in 16 (73%) of 22 patients 21-30 days after the initial CXRs. Bilateral non-diffuse involvement was the commonest finding throughout follow-up, affecting 16 (59%) of 27 patients 11-20 days after the initial CXRs. Bilateral diffuse involvement was seen in five (63%) of eight patients 31-40 days after the initial CXRs. A bilateral diffuse CXR pattern had an odds ratio for mortality of 13 (95% CI=2-78) on worst and 18 (95% CI=3-119) on final CXRs (P-value <0.05).

Conclusion

Initially, normal CXRs are common in MERS-CoV patients. Peripherally located ground-glass and mixed opacities are common on initial and follow-up imaging. The risk of mortality is higher when bilateral diffuse radiographic abnormalities are detected.

A case series of pediatric COVID-19 with complicated symptoms in Iran

People in different age groups are susceptible to SARS-CoV-2 infection as a newly emerging virus. However, the clinical course, symptoms and disease outcome vary from case to case. Although COVID-19 is usually milder in children than adults, some studies reported nonspecific symptoms. Here, we report eight pediatric cases of COVID-19 admitted in the Taleghani Children Hospital in Gorgan city, north of Iran, with complicated symptoms. The current case series poses several challenges to the pediatricians regarding the pediatric cases of COVID-19. As most literature relating to adults are not always transferable to children, clinicians should be warned about such presentations among children with COVID-19.

The reliability of low-dose chest CT for the initial imaging of COVID-19: comparison of structured findings, categorical diagnoses and dose levels

PURPOSE

The widespread use of computed tomography (CT) in COVID-19 may cause adverse biological effects. Many recommend to minimize radiation dose while maintaining diagnostic quality. This study was designed to evaluate the difference between findings of COVID-19 pneumonia on standard and low-dose protocols to provide data on the utility of the latter during initial imaging of COVID-19.

METHODS

Patients suspected of having COVID-19 were scanned with a 128-slices scanner using two consecutive protocols in the same session (standard-dose scan: 120 kV and 300 mA; low-dose scan: 80 kV and 40 mA). Dose data acquisition and analysis was performed using an automated software. High and low-dose examinations were anonymized, shuffled and read by two radiologist with consensus according to a highly structured reporting format that was primarily based on the consensus statement of the RSNA. Accordingly, 8 typical, 2 indeterminate, and 7 atypical findings were investigated. Cases were then assigned to one of the categories: (i) Cov19Typ, typical COVID-19; (ii) Cov19Ind, indeterminate COVID-19; (iii) Cov19Aty, atypical COVID-19; (iv) Cov19Neg, not COVID-19. McNemar test was used to analyze the number of disagreements between standard and low-dose scans regarding paired proportions of structured findings. Inter- test reliability was tested using kappa coefficient.

RESULTS

The study included 740 patients with a mean age of 44.05±16.59 years. The median (minimum-maximum) dose level for standard protocol was 189.98 mGy•cm (98.20-493.54 mGy•cm) and for low-dose protocol was 15.59 mGy•cm (11.59-32.37 mGy•cm) differing by -80 and -254 mGy•cm from pan-European diagnostic reference levels. Only two findings for typical, one finding for indeterminate, and three findings for atypical categories were statistically similar (p > 0.05). The difference in other categories resulted in significantly different final diagnosis for COVID-19 (p < 0.001). Overall, 626 patients received matching diagnoses with the two protocols. According to intertest reliability analysis, kappa value was found to be 0.669 (p < 0.001) to indicate substantial match. CT with standard-dose had a sensitivity of 94% and a specificity of 72%, while CT with low-dose had a sensitivity of 90% and a specificity of 81%.

CONCLUSION

Low kV and mA scans, as used in this study according to scanner manufacturer's global recommendations, may significantly lower exposure levels. However, these scans are significantly inferior in the detection of several individual CT findings of COVID-19 pneumonia, particularly the ones with GGO. Therefore, they should not be used as the protocol of choice in the initial imaging of COVID-19 patients during which higher sensitivity is required.

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review

BACKGROUND

Accurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images.

OBJECTIVE

The aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care.

METHODS

A systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19-related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool.

RESULTS

Of the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non-neural network-based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting.

CONCLUSIONS

Machine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19.

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

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Learning from pathophysiological aspects of COVID-19 clinical, laboratory, and high-resolution CT features: a retrospective analysis of 128 cases by disease severity

BACKGROUND

The classic chest CT imaging features of COVID-19 pneumonia have low specificity due to their similarity with a number of other conditions. So, the goal of the present study is to learn from the pathophysiology of COVID-19 clinical features, laboratory results, and high-resolution CT manifestations in different stages of disease severity to provide significant reference values for diagnosis, prevention, and treatment.

METHODS

This was a multicentered study that included 128 patients. Demographic, clinical, and laboratory data, in addition to chest HRCT findings, were evaluated. According to chest HRCT features, radiologic scoring were grade 1 and 2 for mild grades of the disease, 3 and 4 for moderate grades of the disease, and 5 and 6 for severe grades of the disease.

RESULTS

Patient clinical symptoms ranged between fever, dry cough, muscle ache (myalgia)/fatigue, dyspnea, hyposomia, sore throat, and diarrhea. Lymphocytes and WBCs were significantly lower in patients with severe COVID-19. A significant negative correlation was found with WBCs (r = - 0.245, P = 0.005), lymphocytes% (r = - 0.586, P < 0.001), RBCs (r = - 0.2488, P = 0.005), Hb (gm/dl) (r = - 0.342, P < 0.001), and HCT (r = - 0.377, P < 0.001). Transferrin and CRP were significantly higher in moderate and severe COVID-19 than mild degree and showed a significant positive correlation with CT score (r = 0.356, P < 0.001) and (r = 0.429, P < 0.001), respectively. The most common CT features were peripheral pulmonary GGO and air space consolidation.

CONCLUSION

Clinical features, laboratory assessment, and HRCT imaging had their characteristic signs and performances. Correlating them can make it possible for physicians and radiologists to quickly obtain the final diagnosis and staging of the COVID-19 pneumonia.

From Radiological Manifestations to Pulmonary Pathogenesis of COVID-19: A Bench to Bedside Review

In this review, we aim to assess previous radiologic studies in COVID-19 and suggest a pulmonary pathogenesis based on radiologic findings. Although radiologic features are not specific and there is heterogeneity in symptoms and radiologic and clinical manifestation, we suggest that the dominant pattern of computed tomography is consistent with limited pneumonia, followed by interstitial pneumonitis and organizing pneumonia.

Insights of COVID-19 pandemic impact on anesthetic management for patients undergoing cancer surgery in the National Cancer Institute, Egypt

Abstract

New corona virus disease COVID-19 is a pandemic outbreak viral infection that is highly contagious. The disease can affect any age groups. Majority of patients show mild or no symptoms. Immunocompromised patients and patients with co-morbidities are more vulnerable to have more aggressive affection with higher rate of complications. Thus, cancer patients carry a higher risk of infection. Diseased patient can transmit infection throughout the disease course starting from the incubation period to clinical recovery. All healthcare workers contacting COVID-19-positive patients are at great risk of infection, especially the anesthesiologists who can be exposed to high viral load during airway manipulation. In the National Cancer Institute of Egypt, we apply a protocol to prioritize cases where elective cancer surgeries that would not affect patient prognosis and outcome are postponed during the early phase and peak of the pandemic till reaching a plateau. However, emergency and urgent surgeries that can compromise cancer patient’s life and prognosis take place after the proper assessment of the patient’s condition.

Aim

This review aims to spot the management of cancer patients undergoing surgery during the COVID-19 pandemic in the National Cancer Institute, Egypt.

Lethal COVID-19: Radiologic-Pathologic Correlation of the Lungs

PURPOSE

The purpose of this retrospective study was to correlate CT patterns of fatal cases of coronavirus disease 2019 (COVID-19) with postmortem pathology observations.

MATERIALS AND METHODS

The study included 70 lung lobes of 14 patients who died of reverse-transcription polymerase chain reaction-confirmed COVID-19. All patients underwent antemortem CT and autopsy between March 9 and April 30, 2020. Board-certified radiologists and pathologists performed lobewise correlations of pulmonary observations. In a consensus reading, 267 radiologic and 257 histopathologic observations of the lungs were recorded and systematically graded according to severity. These observations were matched and evaluated.

RESULTS

Predominant CT observations were ground-glass opacities (GGO) (59/70 lobes examined) and areas of consolidation (33/70). The histopathologic observations were consistent with diffuse alveolar damage (70/70) and capillary dilatation and congestion (70/70), often accompanied by microthrombi (27/70), superimposed acute bronchopneumonia (17/70), and leukocytoclastic vasculitis (7/70). Four patients had pulmonary emboli. Bronchial wall thickening at CT histologically corresponded with acute bronchopneumonia. GGOs and consolidations corresponded with mixed histopathologic observations, including capillary dilatation and congestion, interstitial edema, diffuse alveolar damage, and microthrombosis. Vascular alterations were prominent observations at both CT and histopathology.

CONCLUSION

A significant proportion of GGO correlated with the pathologic processes of diffuse alveolar damage, capillary dilatation and congestion, and microthrombosis. Our results confirm the presence and underline the importance of vascular alterations as key pathophysiologic drivers in lethal COVID-19.Supplemental material is available for this article.© RSNA, 2020.

Chest computed tomography findings of COVID-19 pneumonia: pictorial essay with literature review

Available information on chest Computed Tomography (CT) findings of the 2019 novel coronavirus disease (COVID-19) is constantly evolving. Ground glass opacities and consolidation with bilateral and peripheral distribution were reported as the most common CT findings, but also less typical features could be identified. All radiologists should be aware of the imaging spectrum of the COVID-19 pneumonia and imaging changes in the course of the disease. Our aim is to display the chest CT findings at first assessment and follow-up through a pictorial essay, to help in the recognition of these features for an accurate diagnosis.

A diagnostic model for coronavirus disease 2019 (COVID-19) based on radiological semantic and clinical features: a multi-center study

OBJECTIVES

Rapid and accurate diagnosis of coronavirus disease 2019 (COVID-19) is critical during the epidemic. We aim to identify differences in CT imaging and clinical manifestations between pneumonia patients with and without COVID-19, and to develop and validate a diagnostic model for COVID-19 based on radiological semantic and clinical features alone.

METHODS

A consecutive cohort of 70 COVID-19 and 66 non-COVID-19 pneumonia patients were retrospectively recruited from five institutions. Patients were divided into primary (n = 98) and validation (n = 38) cohorts. The chi-square test, Student's t test, and Kruskal-Wallis H test were performed, comparing 1745 lesions and 67 features in the two groups. Three models were constructed using radiological semantic and clinical features through multivariate logistic regression. Diagnostic efficacies of developed models were quantified by receiver operating characteristic curve. Clinical usage was evaluated by decision curve analysis and nomogram.

RESULTS

Eighteen radiological semantic features and seventeen clinical features were identified to be significantly different. Besides ground-glass opacities (p = 0.032) and consolidation (p = 0.001) in the lung periphery, the lesion size (1-3 cm) is also significant for the diagnosis of COVID-19 (p = 0.027). Lung score presents no significant difference (p = 0.417). Three diagnostic models achieved an area under the curve value as high as 0.986 (95% CI 0.966~1.000). The clinical and radiological semantic models provided a better diagnostic performance and more considerable net benefits.

CONCLUSIONS

Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. A model composed of radiological semantic and clinical features has an excellent performance for the diagnosis of COVID-19.

KEY POINTS

• Based on CT imaging and clinical manifestations alone, the pneumonia patients with and without COVID-19 can be distinguished. • A diagnostic model for COVID-19 was developed and validated using radiological semantic and clinical features, which had an area under the curve value of 0.986 (95% CI 0.966~1.000) and 0.936 (95% CI 0.866~1.000) in the primary and validation cohorts, respectively.

Positive Chest CT Features in Patients With COVID-19 Pneumonia and Negative Real-Time Polymerase Chain Reaction Test

Objectives Clinically suspicious novel coronavirus (COVID-19) lung pneumonia can be observed typically on computed tomography (CT) chest scans even in patients with a negative real-time polymerase chain reaction (RT-PCR) test. The purpose of the study was to describe the CT imaging findings of five patients with negative RT-PCR results on initial and repeated testing but a high radiological suspicion of COVID-19 pneumonia. Methods Out of 19 clinically and/or radiologically diagnosed COVID-19 patients from our institution, five patients were selected for our study who had typical findings of COVID-19 on CT scan despite two negative RT-PCR results. Two district general hospital radiologists reviewed the chest CT images without prior knowledge of the RT-PCR test results. Scans were analyzed for the density of opacification and the distribution of disease. Results Out of 19 patients, five (26%) had initial negative RT-PCR test findings but positive CT chest features consistent with COVID-19. All patients had typical CT imaging findings of COVID-19. These included one patient with purely ground-glass opacities (GGO) and four patients with mixed GGO and consolidation. The typical distribution of parenchymal involvement was bilateral, posterior, and peripheral. Of the five patients with negative RT-PCR and positive CT findings, the range of CT severity score was 5 to 14. The median score, seen in three patients, was a score of 5, which corresponded to mild disease. One patient had a score of 8, corresponding to moderate disease, and one patient had severe disease with a score of 14. Conclusion Lung parenchymal changes related to COVID-19 can be seen on chest CT clearly despite repeated RT-PCR negative results.

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

Coronavirus disease 2019 (COVID-19) outbreak, first reported in Wuhan, China, has rapidly swept around the world just within a month, causing global public health emergency. In diagnosis, chest computed tomography (CT) manifestations can supplement parts of limitations of real-time reverse transcription polymerase chain reaction (RT-PCR) assay. Based on a comprehensive literature review and the experience in the frontline, we aim to review the typical and relatively atypical CT manifestations with representative COVID-19 cases at our hospital, and hope to strengthen the recognition of these features with radiologists and help them make a quick and accurate diagnosis.Key Points• Ground glass opacities, consolidation, reticular pattern, and crazy paving pattern are typical CT manifestations of COVID-19.• Emerging atypical CT manifestations, including airway changes, pleural changes, fibrosis, nodules, etc., were demonstrated in COVID-19 patients.• CT manifestations may associate with the progression and prognosis of COVID-19.

Cutaneous manifestations in hospitalized patients diagnosed as COVID-19

Cutaneous manifestations of COVID‐19 disease have not yet been fully described. To describe cutaneous manifestations of COVID‐19 disease in hospitalized patients. We examined the cutaneous manifestations of 210 hospitalized patients. Cutaneous findings were observed during COVID‐19 infection in 52 of the patients. Lesions may be classified as erythematous scaly rash (32.7%), maculopapular rash (23%), urticarial lesions (13.5%), petechial purpuric rash (7.7%), necrosis (7.7%), enanthema and apthous stomatitis (5.8%), vesicular rash (5.8%), pernio (1.9%), and pruritus (1.9%). Cutaneous manifestations were observed statistically significantly more in certain age groups: patients of 55 to 64 and 65 to 74 years of age complained of more cutaneous manifestations than the other age groups. As for gender, there was no significant difference between male and female patients in terms of cutaneus findings. The relationship between comorbidity and dermatological finding status was statistically significant. The relationship increases linearly according to the comorbidities. According to the statistical results, the patients who were hospitalized in the intensive care unit had a higher risk of having cutaneous findings due to COVID‐19 infection. With this study, we may highlight the importance of overlooked dermatological findings in patients that are hospitalized.

Performance of Radiologists in the Evaluation of the Chest Radiography with the Use of a "new software score" in Coronavirus Disease 2019 Pneumonia Suspected Patients

OBJECTIVES

The purpose of this study is to assess the performance of radiologists using a new software called "COVID-19 score" when performing chest radiography on patients potentially infected by coronavirus disease 2019 (COVID-19) pneumonia. Chest radiography (or chest X-ray, CXR) and CT are important for the imaging diagnosis of the coronavirus pneumonia (COVID-19). CXR mobile devices are efficient during epidemies, because allow to reduce the risk of contagion and are easy to sanitize.

MATERIAL AND METHODS

From February-April 2020, 14 radiologists retrospectively evaluated a pool of 312 chest X-ray exams to test a new software function for lung imaging analysis based on radiological features and graded on a three-point scale. This tool automatically generates a cumulative score (0-18). The intra- rater agreement (evaluated with Fleiss's method) and the average time for the compilation of the banner were calculated.

RESULTS

Fourteen radiologists evaluated 312 chest radiographs of COVID-19 pneumonia suspected patients (80 males and 38 females) with an average age of 64, 47 years. The inter-rater agreement showed a Fleiss' kappa value of 0.53 and the intra-group agreement varied from Fleiss' Kappa value between 0.49 and 0.59, indicating a moderate agreement (considering as "moderate" ranges 0.4-0.6). The years of work experience were irrelevant. The average time for obtaining the result with the automatic software was between 7 s (e.g., zero COVID-19 score) and 21 s (e.g., with COVID-19 score from 6 to 12).

CONCLUSION

The use of automatic software for the generation of a CXR "COVID-19 score" has proven to be simple, fast, and replicable. Implementing this tool with scores weighed on the number of lung pathological areas, a useful parameter for clinical monitoring could be available.

CT chest of COVID-19 patients: what should a radiologist know?

Background

Main body

Conclusion

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists

COVID-19 is an emerging infection caused by a novel coronavirus that is moving so rapidly that on 30 January 2020 the World Health Organization declared the outbreak a Public Health Emergency of International Concern and on 11 March 2020 as a pandemic. An early diagnosis of COVID-19 is crucial for disease treatment and control of the disease spread. Real-time reverse-transcription polymerase chain reaction (RT-PCR) demonstrated a low sensibility; therefore chest computed tomography (CT) plays a pivotal role not only in the early detection and diagnosis, especially for false negative RT-PCR tests, but also in monitoring the clinical course and in evaluating the disease severity. This paper reports the CT findings with some hints on the temporal changes over the course of the disease: the CT hallmarks of COVID-19 are bilateral distribution of ground glass opacities with or without consolidation in the posterior and peripheral lung, but the predominant findings in later phases include consolidations, linear opacities, “crazy-paving” pattern, “reversed halo” sign and vascular enlargement. The CT findings of COVID-19 overlap with the CT findings of other diseases, in particular the viral pneumonia including influenza viruses, parainfluenza virus, adenovirus, respiratory syncytial virus, rhinovirus, human metapneumovirus, etc. There are differences as well as similarities in the CT features of COVID-19 compared with those of the severe acute respiratory syndrome. The aim of this article is to review the typical and atypical CT findings in COVID-19 patients in order to help radiologists and clinicians to become more familiar with the disease.

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection

In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, "crazy-paving" pattern and the "reverse halo" sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

Diagnostic value and key features of computed tomography in Coronavirus Disease 2019

On 31 December 2019, a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan, Hubei province, China, and caused the outbreak of the Coronavirus Disease 2019 (COVID-19). To date, computed tomography (CT) findings have been recommended as major evidence for the clinical diagnosis of COVID-19 in Hubei, China. This review focuses on the imaging characteristics and changes throughout the disease course in patients with COVID-19 in order to provide some help for clinicians. Typical CT findings included bilateral ground-glass opacity, pulmonary consolidation, and prominent distribution in the posterior and peripheral parts of the lungs. This review also provides a comparison between COVID-19 and other diseases that have similar CT findings. Since most patients with COVID-19 infection share typical imaging features, radiological examinations have an irreplaceable role in screening, diagnosis and monitoring treatment effects in clinical practice.

Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.