Diagnostic Performance of CT and Reverse Transcriptase Polymerase Chain Reaction for Coronavirus Disease 2019: A Meta-Analysis

An updated review of pulmonary radiological features of acute and chronic COVID-19

PURPOSE OF REVIEW

Significant progress has been made in our understanding of the acute and chronic clinical and radiological manifestations of coronavirus-19 (COVID-19). This article provides an updated review on pulmonary COVID-19, while highlighting the key imaging features that can identify and distinguish acute COVID-19 pneumonia and its chronic sequelae from other diseases.

RECENT FINDINGS

Acute COVID-19 pneumonia typically presents with manifestations of organizing pneumonia on computed tomography (CT). In cases of severe disease, patients clinically progress to acute respiratory distress syndrome, which manifests as diffuse alveolar damage on CT. The most common chronic imaging finding is ground-glass opacities, which commonly resolves, as well as subpleural bands and reticulation. Pulmonary fibrosis is an overall rare complication of COVID-19, with characteristic features, including architectural distortion, and traction bronchiectasis.

SUMMARY

Chest CT can be a helpful adjunct tool in both diagnosing and managing acute COVID-19 pneumonia and its chronic sequelae. It can identify high-risk cases and guide decision-making, particularly in cases of severe or complicated disease. Follow-up imaging can detect persistent lung abnormalities associated with long COVID and guide appropriate management.

Potential role of lung ultrasonography in outpatient follow-up of patients with COVID-19. A systematic review

INTRODUCTION AND AIM

Currently, the usefulness of lung ultrasound in the follow-up of patients after hospital discharge for SARS-CoV-2 pneumonia is not well known. The main objective of this systematic review is to investigate the persistence of alterations in lung ultrasound of patients who have had COVID-19 pneumonia.

METHODS

A systematic review has been carried out following the PRISMA regulations in the PubMed, EMBASE, Web of Science and Google Scholar database from January 2020 to May 2023 using the combination of MeSH terms: "lung ultrasound", "ultrasonography", "lung alterations", "persistence", "follow-up", "consequences", "hospital discharge", "COVID", "COVID-19", "SARS-CoV-2". Studies were selected that described alterations in the lung ultrasound of patients after having suffered from COVID-19 pneumonia. The JBI Critical Appraisal Tools were used to assess the risk of bias of the studies. No meta-analysis techniques were performed, the results being compared narratively.

RESULTS

From two to six months after COVID-19 pneumonia, pulmonary ultrasound abnormalities appear frequently and are proportional to the intensity of the initial episode. The most frequent anomalies are irregularities in the pleural line, the presence of B lines and/or subpleural consolidations, predominantly in the basal regions of the thorax. These findings seem to correlate with those of the chest CT.

CONCLUSIONS

Lung ultrasound offers technical and economic advantages that should be considered for the study of patients after hospital discharge for COVID-19.

Comparison of different T cell assays for the retrospective determination of SARS-CoV-2 infection

It is important to be able to retrospectively determine severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections with high accuracy, both for post-coronavirus disease 2019 (COVID-19) epidemiological studies, and to distinguish between Long COVID and other multi-syndromic diseases that have overlapping symptoms. Although serum antibody levels can be measured to retrospectively diagnose SARS-CoV-2 infections, peptide stimulation of memory T cell responses is a more sensitive approach. This is because robust memory T cells are generated after SARS-CoV-2 infection and persist even after antibodies wane below detectability thresholds. In this study, we compare T cell responses using FluoroSpot-based methods and overnight stimulation of whole blood with SARS-CoV-2 peptides followed by an ELISA. Both approaches have comparable sensitivity and specificity but require different equipment and samples to be used. Furthermore, the elimination of peptides that cross-react with other coronaviruses increases the assay specificity but trades off some sensitivity. Finally, this approach can be used on archival, cryopreserved PBMCs. This work shows comparative advantages for several methods to measure SARS-CoV-2 T cell responses that could be utilized by any laboratory studying the effects of the coronavirus disease 2019 pandemic.

Evaluating the Accuracy of Chest CT in Detecting COVID-19 Through Tracheobronchial Wall Thickness: Insights From Emergency Department Patients in Mid-2023

Background The post-pandemic phase of the coronavirus infectious disease that emerged in 2019 (COVID-19) has necessitated updates in radiology, with emerging evidence suggesting tracheobronchial wall thickness as a potential new diagnostic marker. Purpose To evaluate the accuracy of chest computed tomography (CT) scans in identifying COVID-19 by assessing tracheobronchial wall thickness in mid-2023. Material and methods A retrospective review was conducted on 60 patients who underwent thoracoabdominal CT and the severe acute respiratory syndrome coronavirus (SARS-CoV-2) antigen tests during emergency visits between June and August 2023. Tracheobronchial wall thickness was measured using a 4-point scale (1=no thickening, 2=mild, 3=moderate, 4=significant). Lung assessment employed the COVID-19 Reporting and Data System (CO-RADS). Patients were classified based on antigen test results. The Mann-Whitney U test and Fisher's exact test compared characteristics and CT findings. Diagnostic performance was evaluated using the area under the receiver operating characteristic curves (AUC). Results The SARS-CoV-2-positive group showed significantly thicker tracheobronchial walls (median 1.5 mm vs. 1.2 mm, P < 0.001), especially in the trachea's membranous wall (median 1.2 mm vs. 0.9 mm, P < 0.001) and higher scores (median 3 vs. 2, P < 0.001). CO-RADS scores showed no significant difference. Quantitative and qualitative wall thickness assessments demonstrated high diagnostic value, with AUCs of 0.90 and 0.94, and accuracies of 85% and 87%, respectively. Conclusion Tracheobronchial wall thickness on chest CT exhibited high diagnostic accuracy, establishing it as a reliable marker for COVID-19 detection in mid-2023.

A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis

BACKGROUND

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

METHOD

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

FINDINGS

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

CONCLUSION

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

Weight loss treatment for COVID-19 in patients with NCDs: a pilot prospective clinical trial

COVID-19 comorbid with noncommunicable chronic diseases (NCDs) complicates the diagnosis, treatment, and prognosis, and increases the mortality rate. The aim is to evaluate the effects of a restricted diet on clinical/laboratory inflammation and metabolic profile, reactive oxygen species (ROS), and body composition in patients with COVID-19 comorbid with NCDs. We conducted a 6-week open, pilot prospective controlled clinical trial. The study included 70 adult patients with COVID-19 comorbid with type 2 diabetes (T2D), hypertension, or nonalcoholic steatohepatitis (NASH).

INTERVENTIONS

a restricted diet including calorie restriction, hot water drinking, walking, and sexual self-restraint.

PRIMARY ENDPOINTS

COVID-19 diagnosis by detecting SARS-CoV-2 genome by RT-PCR; weight loss in Main group; body temperature; C-reactive protein. Secondary endpoints: the number of white blood cells; erythrocyte sedimentation rate; adverse effects during treatment; fasting blood glucose, glycosylated hemoglobin A1c (HbA1c), systolic/diastolic blood pressure (BP); blood lipids; ALT/AST, chest CT-scan. In Main group, patients with overweight lost weight from baseline (- 12.4%; P < 0.0001); 2.9% in Main group and 7.2% in Controls were positive for COVID-19 (RR: 0.41, CI: 0.04-4.31; P = 0.22) on the 14th day of treatment. Body temperature and C-reactive protein decreased significantly in Main group compared to Controls on day 14th of treatment (P < 0.025). Systolic/diastolic BP normalized (P < 0.025), glucose/lipids metabolism (P < 0.025); ALT/AST normalized (P < 0.025), platelets increased from baseline (P < 0.025), chest CT (P < 0.025) in Main group at 14 day of treatment. The previous antidiabetic, antihypertensive, anti-inflammatory, hepatoprotective, and other symptomatic medications were adequately decreased to completely stop during the weight loss treatment. Thus, the fast weight loss treatment may be beneficial for the COVID-19 patients with comorbid T2D, hypertension, and NASH over traditional medical treatment because, it improved clinical and laboratory/instrumental data on inflammation; glucose/lipid metabolism, systolic/diastolic BPs, and NASH biochemical outcomes, reactive oxygen species; and allowed patients to stop taking medications.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05635539 (02/12/2022): https://clinicaltrials.gov/ct2/show/NCT05635539?term=NCT05635539&draw=2&rank=1 .

Interest of Chest CT to Assess the Prognosis of SARS-CoV-2 Pneumonia: An In-Hospital-Based Experience in Sub-Saharan Africa

Methods

We included all patients with respiratory symptoms (dyspnea, fever, and cough) and/or respiratory failure admitted to the SOS Médecins de nuit SARL hospital, DR Congo, during the 2nd and 3rd waves of the COVID-19 pandemic. The diagnosis of COVID-19 was established based on RT-PCR anti-SARS-CoV-2 tests (G1 (RT-PCR positive) vs. G2 (RT-PCR negative)), and all patients had a chest CT on the day of admission. We retrieved the digital files of patients, precisely the clinical, biological, and chest CT parameters of the day of admission as well as the vital outcome (survival or death). Chest CT were read by a very high-definition console using Advantage Windows software and exported to the hospital network using the RadiAnt DICOM viewer. To determine the threshold for the percentage of lung lesions associated with all-cause mortality, we used ROC curves. Factors associated with death, including chest CT parameters, were investigated using logistic regression analysis.

Results

The study included 200 patients (average age 56.2 ± 15.2 years; 19% diabetics and 4.5% obese), and COVID-19 was confirmed among 56% of them (G1). Chest CT showed that ground glass (72.3 vs. 39.8%), crazy paving (69.6 vs. 17.0%), and consolidation (83.9 vs. 22.7%), with bilateral and peripheral locations (68.8 vs. 30.7%), were more frequent in G1 vs. G2 (p < 0.001). No case of pulmonary embolism and fibrosis had been documented. The lung lesions affecting 30% of the parenchyma were informative in predicting death (area under the ROC curve at 0.705, p = 0.017). In multivariate analysis, a percentage of lesions affecting 50% of the lung parenchyma increased the risk of dying by 7.194 (1.656-31.250).

Conclusion

The chest CT demonstrated certain characteristic lesions more frequently in patients in whom the diagnosis of COVID-19 was confirmed. The extent of lesions affecting at least half of the lung parenchyma from the first day of admission to hospital increases the risk of death by a factor of 7.

Double-Negative Results Matter: A Reevaluation of Sensitivities for Detecting SARS-CoV-2 Infection Using Saliva Versus Nasopharyngeal Swabs

In a recent systematic review, Bastos et al. (Ann Intern Med. 2021;174(4):501-510) compared the sensitivities of saliva sampling and nasopharyngeal swabs in the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection by assuming a composite reference standard defined as positive if either test is positive and negative if both tests are negative (double negative). Even under a perfect specificity assumption, this approach ignores the double-negative results and risks overestimating the sensitivities due to residual misclassification. In this article, we first illustrate the impact of double-negative results in the estimation of the sensitivities in a single study, and then propose a 2-step latent class meta-analysis method for reevaluating both sensitivities using the same published data set as that used in Bastos et al. by properly including the observed double-negative results. We also conduct extensive simulation studies to compare the performance of the proposed method with Bastos et al.'s method for varied levels of prevalence and between-study heterogeneity. The results demonstrate that the sensitivities are overestimated noticeably using Bastos et al.'s method, and the proposed method provides a more accurate evaluation with nearly no bias and close-to-nominal coverage probability. In conclusion, double-negative results can significantly impact the estimated sensitivities when a gold standard is absent, and thus they should be properly incorporated.

Potential diagnostic application of a novel deep learning- based approach for COVID-19

COVID-19 is a highly communicable respiratory illness caused by the novel coronavirus SARS-CoV-2, which has had a significant impact on global public health and the economy. Detecting COVID-19 patients during a pandemic with limited medical facilities can be challenging, resulting in errors and further complications. Therefore, this study aims to develop deep learning models to facilitate automated diagnosis of COVID-19 from CT scan records of patients. The study also introduced COVID-MAH-CT, a new dataset that contains 4442 CT scan images from 133 COVID-19 patients, as well as 133 CT scan 3D volumes. We proposed and evaluated six different transfer learning models for slide-level analysis that are responsible for detecting COVID-19 in multi-slice spiral CT. Additionally, multi-head attention squeeze and excitation residual (MASERes) neural network, a novel 3D deep model was developed for patient-level analysis, which analyzes all the CT slides of a given patient as a whole and can accurately diagnose COVID-19. The codes and dataset developed in this study are available at https://github.com/alrzsdgh/COVID . The proposed transfer learning models for slide-level analysis were able to detect COVID-19 CT slides with an accuracy of more than 99%, while MASERes was able to detect COVID-19 patients from 3D CT volumes with an accuracy of 100%. These achievements demonstrate that the proposed models in this study can be useful for automatically detecting COVID-19 in both slide-level and patient-level from patients' CT scan records, and can be applied for real-world utilization, particularly in diagnosing COVID-19 cases in areas with limited medical facilities.

Comparison of Computed Tomography Findings between Adult and Pediatric COVID-19 Patients

PURPOSE

This study aims to compare chest computed tomography (CT) findings between adult and pediatric patients with coronavirus disease-19 (COVID-19) pneumonia.

MATERIALS AND METHODS

This study included 30 pediatric patients aged 1 to 17 years and 30 adult patients over 18 years of age with COVID-19 pneumonia confirmed by reverse transcriptase-polymerase chain reaction (RT-PCR) who have findings related to COVID-19 on Chest Computed Tomography. The CT findings of adult and pediatric patients were compared with a z-test.

RESULTS

Bilateral involvement (p:0.00056), involvement in all five lobes (p<0.00001), and central and peripheral involvement (p:0.01928) were significantly higher in the adult group compared to the pediatric group. In the pediatric group, the frequency of unilateral involvement (p:0.00056), involvement of solitary lobe (p:0.00132), and peripheral involvement (p: 0.01928) were significantly higher than in the adult group. The most common parenchymal finding in adults and pediatric patients was ground-glass opacities (100% and 83%, respectively). Among the parenchymal findings in adults, ground-glass opacities with consolidation (63%) were the second most common finding, followed by air bronchogram (60%) in adults, while in pediatric patients, halo sign (27%) and nodule (27%) were the second most common, followed by the ground-glass opacities with consolidation (23%).

CONCLUSION

The CT findings of pediatric COVID-19 patients must be well-known as the course of the disease is usually less severe, and the radiological findings are uncertain when compared with adults.

Correlation of Computed Tomography (CT) Severity Score With Laboratory and Clinical Parameters and Outcomes in Coronavirus Disease 2019 (COVID-19)

BACKGROUND

 Coronavirus disease 2019 (COVID-19) is a potentially lethal respiratory illness caused by a newly identified coronavirus called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the novelty of the virus, high caseloads, and increasing turnaround time for reverse transcriptase-polymerase chain reaction (RT-PCR) results, accurate information about the clinical course and prognosis of individual patients was largely unknown. This has forced physicians all over the world to brainstorm attempts to come up with reliable indicators like chest high-resolution computed tomography (HRCT) for any changes suggestive of COVID-19; surrogate laboratory parameters such as C-reactive protein (CRP), ferritin, D-dimer, lactate dehydrogenase (LDH), or interleukin-6 (IL-6) for assessing the severity of the disease; and other organ-specific tests to identify the multiorgan involvement in severe-to-critical COVID-19. Chest computed tomography (CT) scans play a significant role in the management of COVID-19 disease and serve as an indicator of disease severity and its possible outcome, which might help in the early identification of patients who might need critical care and earlier prognostication.

METHODS

A retrospective observational study was conducted at a single center in a level 3 critical care unit (CCU) of a 750-bed teaching hospital in Hyderabad, Telangana, India, over a period of six months. All RT-PCR-positive COVID-19 patients admitted to the CCU with CT chest performed within 24 hours of admission were screened for eligibility for this study. CT severity scoring was based on chest HRCT or CT.

RESULTS

Of the 110 patients, a majority (36.36%) were aged between 61 and 70 years. The mean age of our study population was 59.65±11.88 years. Of the 110 patients, the majority were admitted to the hospital for 22-28 days (24.55%), followed by 8-14 days (22.72%), and 21.82% were admitted for one day. Of the 110 patients, a majority were admitted to the CCU for seven days (41.82%), followed by 15-21 days (24.55%); and 19.09% were admitted for 8-14 days. Most of the patients were discharged (65.45%), and we had a 34.55% mortality rate in our study. We found a significant association between chest CT severity score (CTSS) and the age of the patient, duration of hospital stay, and duration of CCU stay using multivariate regression analysis.

CONCLUSION

CTSS could be greatly helpful for the screening and early identification of the disease, especially in those patients awaiting an RT-PCR report or with negative RT-PCR, which would lead to appropriate isolation and treatment measures. Early detection could also help assess the progression of the disease, alter the course of management at the earliest point possible, and improve the prognostication of COVID-19 patients.

Segmentation of lung lobes and lesions in chest CT for the classification of COVID-19 severity

To precisely determine the severity of COVID-19-related pneumonia, computed tomography (CT) is an imaging modality beneficial for patient monitoring and therapy planning. Thus, we aimed to develop a deep learning-based image segmentation model to automatically assess lung lesions related to COVID-19 infection and calculate the total severity score (TSS). The entire dataset consisted of 124 COVID-19 patients acquired from Chulabhorn Hospital, divided into 28 cases without lung lesions and 96 cases with lung lesions categorized severity by radiologists regarding TSS. The model used a 3D-UNet along with DenseNet and ResNet models that had already been trained to separate the lobes of the lungs and figure out the percentage of lung involvement due to COVID-19 infection. It also used the Dice similarity coefficient (DSC) to measure TSS. Our final model, consisting of 3D-UNet integrated with DenseNet169, achieved segmentation of lung lobes and lesions with the Dice similarity coefficients of 91.52% and 76.89%, respectively. The calculated TSS values were similar to those evaluated by radiologists, with an R2 of 0.842. The correlation between the ground-truth TSS and model prediction was greater than that of the radiologist, which was 0.890 and 0.709, respectively.

COVID-19 Diagnosis in Patients With Acute Abdominal Pain Without Respiratory Symptoms: A UK Emergency General Surgical Unit Experience

BACKGROUND

Recent evidence has emerged reporting atypical clinical symptoms of the novel coronavirus (COVID-19). There is a sparsity of existing studies examining COVID-19-related abdominal pain and the role of investigative imaging for the virus in these patients. Study aims were to determine COVID-19 incidence in those with acute abdominal pain in the absence of respiratory symptoms and to assess the diagnostic performance of CT thoracic imaging in such patients.

METHODS

Retrospective analysis of all consecutive patients admitted to our emergency general surgical unit between 1st March 2020 and 31st May 2020 was performed. In adherence with national guidelines, all patients underwent nasal and oro-pharyngeal COVID-19 RT-PCR swabs as well as thoracic and abdominal computed tomography (CT) on admission.

RESULTS

From 112 patients admitted with acute abdominal pain in the absence of respiratory symptoms, 16 (14.3%) tested positive for COVID-19 on RT-PCR swab testing. Overall, 50% (8/16) of these patients had no intra-abdominal pathology on CT. The sensitivity and specificity of CT thoracic imaging for diagnosing COVID-19 was 43.8% and 91.7%, respectively. Patients with positive COVID-19 swabs had higher C-reactive protein levels, lower potassium levels and a higher proportion of those with a low lymphocyte count.

DISCUSSION

One in seven patients with abdominal pain without any respiratory symptoms tested positive for COVID-19. Half of these patients represented COVID-19 manifesting primarily as acute abdominal pain. Combined swab testing and CT imaging should be performed in all abdominal pain presentations due to the varying diagnostic performance of thoracic CT in diagnosing COVID-19.

The importance of prevalence and pre-test probability on the microbiological diagnosis of SARS-CoV-2: the case of Spain in 2020

OBJECTIVE

The aim of this work was to estimate the conditioned probability for the diagnosis of SARS-CoV-2 infection with reverse transcription polymerase chain reaction (RT-PCR), viral antigen rapid diagnostic tests (Ag-RDT), and antibody detection tests depending on the prevalence in the specific healthcare settings in Spain in 2020, and on the pre-test probability (PTP) according to the clinical situation, age and unknown or close contacts of the patient.

METHODS

Performance parameters of tests were obtained from literature. Prevalence data and PTP were obtained from Spanish sources and a survey, respectively. The post-test probability is the positive predictive value (PPV) when test is positive. For negative result, we also calculated the probability of having the infection (false negatives).

RESULTS

For both RT-PCR and viral Ag-RDT, the lowest PPV values were for the population screenings. This strategy proved to be useful in ruling out infection but generates a high number of false positives. At individual level, both tools provided high PPV (≥ 97%) when the PTP values are over 35%. In seroprevalence studies, though the specificity of IgG alone tests is high, under low seroprevalence, false positives cannot be avoided. Total antibodies tests are useful for diagnosis of COVID-19 in those doubtful cases with RT-PCR or Ag-RDT tests being repeatedly negative.

CONCLUSIONS

The interpretating of results depends not only on the accuracy of the test, but also on the prevalence of the infection in different settings, and the PTP associated to the patient before performing the test.

Hybridization chain reaction cascaded amplification platform for sensitive detection of pathogen

Rapid, sensitive, and accurate identification of pathogens is vital for preventing and controlling fish disease, reducing economic losses in aquaculture, and interrupting the spread of food-borne diseases in human populations. Herein, we proposed a hybridization chain reaction (HCR) cascaded dual-signal amplification platform for the ultrasensitive and specific detection of pathogenic microorganisms. A couple of specific primers for target bacterial 16S rRNAs were used to obtain amplified target single-stranded DNAs (AT-ssDNA). Then, AT-ssDNA initiated HCR amplification along with the opening of fluorophore (FAM) and a quencher (BHQ1) labeled hairpin reporter probe (H1), and the FAM fluorescence signal recovered. The proposed strategy could achieve a detection limit down to 0.31 CFU/mL for Staphylococcus aureus (S. aureus), 0.49 CFU/mL for Escherichia coli (E. coli) in buffer, and a linear range from 1 to 1 × 10⁶ CFU/mL for S. aureus, 1 to 1 × 10⁷ CFU/mL for E. coli. Furthermore, this platform enabled sensitive and precise detection of pathogenic microorganisms in complex samples such as fish blood and different organ tissues (large intestine, gallbladder, heart, liver, ren, gill, skin), which shows great potential in disease prevention and control in aquatic products.

Diagnostic performance of standardized typical CT findings for COVID-19: a systematic review and meta-analysis

OBJECTIVE

To meta-analyze diagnostic performance measures of standardized typical CT findings for COVID-19 and examine these measures by region and national income.

METHODS

MEDLINE and Embase were searched from January 2020 to April 2022 for diagnostic studies using the Radiological Society of North America (RSNA) classification or the COVID-19 Reporting and Data System (CO-RADS) for COVID-19. Patient and study characteristics were extracted. We pooled the diagnostic performance of typical CT findings in the RSNA and CO-RADS systems and interobserver agreement. Meta-regression was performed to examine the effect of potential explanatory factors on the diagnostic performance of the typical CT findings.

RESULTS

We included 42 diagnostic performance studies with 6777 PCR-positive and 9955 PCR-negative patients from 18 developing and 24 developed countries covering the Americas, Europe, Asia, and Africa. The pooled sensitivity was 70% (95% confidence interval [CI]: 65%, 74%; I² = 92%), and the pooled specificity was 90% (95% CI 86%, 93%; I² = 94%) for the typical CT findings of COVID-19. The sensitivity and specificity of the typical CT findings did not differ significantly by national income and the region of the study (p > 0.1, respectively). The pooled interobserver agreement from 19 studies was 0.72 (95% CI 0.63, 0.81; I² = 99%) for the typical CT findings and 0.67 (95% CI 0.61, 0.74; I² = 99%) for the overall CT classifications.

CONCLUSION

The standardized typical CT findings for COVID-19 provided moderate sensitivity and high specificity globally, regardless of region and national income, and were highly reproducible between radiologists.

CRITICAL RELEVANCE STATEMENT

Standardized typical CT findings for COVID-19 provided a reproducible high diagnostic accuracy globally.

KEY POINTS

Standardized typical CT findings for COVID-19 provide high sensitivity and specificity. Typical CT findings show high diagnosability regardless of region or income. The interobserver agreement for typical findings of COVID-19 is substantial.

COVID-19 seroprevalence among local authority workers from Orléans Métropole, the Community of Communes of the Terres du Val de Loire, the local public service management centre of the Loiret department and the Region Centre Val de Loire: a prospective epidemiological study

OBJECTIVE

To evaluate the SARS-CoV-2 seroprevalence among local authority workers, depending on their position and potential interaction with the public.

METHODS

A cohort of volunteer participants was recruited among local authority workers of the Centre Val de Loire region in France, to be tested using a rapid serological test (COVID-PRESTO). The collected data were analysed by comparing different parameters including, gender, age, position held, and contact or not with the public. The study was carried out from August to December 2020 and included 3228 participants (n=3228), from 18 to 65 years old.

RESULTS

The seroprevalence of SARS-CoV-2 among local authority workers was estimated at 3.04%. No significant difference could be observed according to the position held by the workers and whether they were or not in contact with the public. Nevertheless, a significant difference was observed between the different investigating centres, in correlation with the geographical location.

CONCLUSION

Contact with members of the public was not a critical parameter for SARS-CoV-2 seroprevalence as long as protective measures are applied. Among the population included in the study, childcare workers were more at risk of getting infected by the virus.

TRIAL REGISTRATION NUMBER

NCT04387968.

Is it necessary to define new diagnostic reference levels during pandemics like the Covid19-?

Introduction

This study intended to assess the dose length product (DLP), effective cumulative radiation dose (E.D.), and additional cancer risk (ACR) due to a chest CT scan to detect or follow up the Covid-19 disease in four university-affiliated hospitals that used different imaging protocols. Indeed, this study aimed to examine the differences in decision-making between different imaging centers in choosing chest CT imaging protocols during the pandemic, and to assess whether a new diagnostic reference level (DRL) is needed in pandemic situations.

Methods

This retrospective study assessed the E.D. of all chest imagings for Covid-19 for six months in four different hospitals in our country. Imaging parameters and DLP (mGy.cm) were recorded. The E.D.s and ACRs from chest CT scans were calculated using an online calculator.

Results

Thousand-six hundred patients were included in the study. The mean cumulative dose due to chest CT was 3.97 mSv which might cause 2.59 × 10^-2 ACR. The mean cumulative E.D. in different hospitals was in the range of 1.96-9.51 mSv.

Conclusions

The variety of mean E.D.s shows that different hospitals used different imaging protocols. Since there is no defined DRL in the pandemic, some centers use routine protocols, and others try to reduce the dose but insufficiently.In pandemics such as Covid-19, when CT scan is used for screening or follow-up, DLPs can be significantly lower than in normal situations. Therefore, international regularized organizations such as the international atomic energy agency (IAEA) or the international commission on radiological protection (IRCP) should provide new DRL ranges.

Quantitative Analysis of Image Quality in Low-Dose Computed Tomography Imaging for COVID-19 Patients

Background

Computed tomography (CT) scan is one of the main tools to diagnose and grade COVID-19 progression. To avoid the side effects of CT imaging, low-dose CT imaging is of crucial importance to reduce population absorbed dose. However, this approach introduces considerable noise levels in CT images.

Methods

In this light, we set out to simulate four reduced dose levels (60% dose, 40% dose, 20% dose, and 10% dose) of standard CT imaging using Beer-Lambert's law across 49 patients infected with COVID-19. Then, three denoising filters, namely Gaussian, bilateral, and median, were applied to the different low-dose CT images, the quality of which was assessed prior to and after the application of the various filters via calculation of peak signal-to-noise ratio, root mean square error (RMSE), structural similarity index measure, and relative CT-value bias, separately for the lung tissue and whole body.

Results

The quantitative evaluation indicated that 10%-dose CT images have inferior quality (with RMSE = 322.1 ± 104.0 HU and bias = 11.44% ± 4.49% in the lung) even after the application of the denoising filters. The bilateral filter exhibited superior performance to suppress the noise and recover the underlying signals in low-dose CT images compared to the other denoising techniques. The bilateral filter led to RMSE and bias of 100.21 ± 16.47 HU and - 0.21% ± 1.20%, respectively, in the lung regions for 20%-dose CT images compared to the Gaussian filter with RMSE = 103.46 ± 15.70 HU and bias = 1.02% ± 1.68% and median filter with RMSE = 129.60 ± 18.09 HU and bias = -6.15% ± 2.24%.

Conclusions

The 20%-dose CT imaging followed by the bilateral filtering introduced a reasonable compromise between image quality and patient dose reduction.

Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard

BACKGROUND

The gold-standard method for establishing a microbiological diagnosis of COVID-19 is reverse-transcriptase polymerase chain reaction (RT-PCR). This study aimed to evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard.

METHODS

Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong versus weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent).

RESULTS

With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5-99.5%) sensitivity, 70% (95% CI 65.8-74.2%) specificity, 85.5% (95% CI 83.4-87.7%) accuracy, PPV of 79.7% (95% CI 76.6-82.7%) and NPV of 97.6% (95% CI 95.9-99.2%). Similar performance was observed when evaluated in the subgroups of patients admitted with mild/moderate respiratory disfunction, and severe respiratory disfunction.

CONCLUSION

The proposed set of clinical-radiological criteria were accurate in identifying patients with strong versus weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.

Preoperative SARS-CoV-2 Infection Screening before Thoracic Surgery during COVID-19 Pandemic: A Multicenter Retrospective Study

OBJECTIVES

During coronavirus disease (COVID-19) pandemic, preoperative screening before thoracic surgery is paramount in order to protect patients and staff from undetected infections. This study aimed to determine which preoperative COVID-19 screening tool was the most effective strategy before thoracic surgery.

METHODS

This retrospective cohort multicenter study was performed at 3 Italian thoracic surgery centers. All adult patients scheduled for thoracic surgery procedures from 4th March until 24th April, 2020, and submitted to COVID-19 preoperative screenings were included. The primary outcome was the yield of screening of the different strategies.

RESULTS

A total of 430 screenings were performed on 275 patients; 275 anamnestic questionnaires were administered. 77 patients were screened by an anamnestic questionnaire and reverse transcriptase polymerase chain reaction (RT-PCR). 78 patients were selected to combine screening with anamnestic questionnaire and chest computed tomography (CT). The positive yield of screening using a combination of anamnestic questionnaire and RT-PCR was 7.8% (95% CI: 2.6-14.3), while using a combination of anamnestic questionnaire and chest CT was 3.8% (95% CI: 0-9). Individual yields were 1.1% (95% CI: 0-2.5) for anamnestic questionnaire, 5.2% (95% CI: 1.3-11.7) for RT-PCR, and 3.8% (95% CI: 0-9).

CONCLUSIONS

The association of anamnestic questionnaire and RT-PCR is able to detect around 8 positives in 100 asymptomatic patients. This combined strategy could be a valuable preoperative SARS-CoV-2 screening tool before thoracic surgery.

Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects.

METHODS

The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool.

RESULTS

Seventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I² =83), 0.86 (95% CI 0.76-0.92, I² =96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I² = 41), 0.79 (95% CI 0.72-0.85, I² = 88), and 0.84 (95% CI 0.81-0.87), respectively.

DISCUSSION

Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis.

CONCLUSION

Early prediction of prognosis is needed to deliver optimal care and timely stratification of patients.  CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.

Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database

Background and Objective

The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-polymerase chain reaction (RT-PCR), though considered the standard testing measure, has low sensitivity and is time-consuming, which restricts the fast screening of individuals. Therefore, computer tomography (CT) is used to complement the traditional approaches and provide fast and effective screening over other diagnostic methods. This work aims to appraise the importance of chest CT findings of COVID-19 and post-COVID in the diagnosis and prognosis of infected patients and to explore the ways and means to integrate CT findings for the development of advanced Artificial Intelligence (AI) tool-based predictive diagnostic techniques.

Methods

The retrospective study includes a 188 patient database with COVID-19 infection confirmed by RT-PCR testing, including post-COVID patients. Patients underwent chest high-resolution computer tomography (HRCT), where the images were evaluated for common COVID-19 findings and involvement of the lung and its lobes based on the coverage region. The radiological modalities analyzed in this study may help the researchers in generating a predictive model based on AI tools for further classification with a high degree of reliability.

Results

Mild to moderate ground glass opacities (GGO) with or without consolidation, crazy paving patterns, and halo signs were common COVID-19 related findings. A CT score is assigned to every patient based on the severity of lung lobe involvement.

Conclusion

Typical multifocal, bilateral, and peripheral distributions of GGO are the main characteristics related to COVID-19 pneumonia. Chest HRCT can be considered a standard method for timely and efficient assessment of disease progression and management severity. With its fusion with AI tools, chest HRCT can be used as a one-stop platform for radiological investigation and automated diagnosis system.

COVID-19 diagnostic approaches with an extensive focus on computed tomography in accurate diagnosis, prognosis, staging, and follow-up

Although a long time has passed since its outbreak, there is currently no specific treatment for COVID-19, and it seems that the most appropriate strategy to combat this pandemic is to identify and isolate infected individuals. Various clinical diagnosis methods such as molecular techniques, serologic assays, and imaging techniques have been developed to identify suspected patients. Although reverse transcription-quantitative PCR (RT-qPCR) has emerged as a reference standard method for diagnosis of SARS-CoV-2, the high rate of false-negative results and limited supplies to meet current demand are the main shortcoming of this technique. Based on a comprehensive literature review, imaging techniques, particularly computed tomography (CT), show an acceptable level of sensitivity in the diagnosis and follow-up of COVID-19. Indeed, because lung infection or pneumonia is a common complication of COVID-19, the chest CT scan can be an alternative testing method in the early diagnosis and treatment assessment of the disease. In this review, we summarize all the currently available frontline diagnostic tools for the detection of SARS-CoV-2-infected individuals and highlight the value of chest CT scan in the diagnosis, prognosis, staging, management, and follow-up of infected patients.

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing

(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT-PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT-PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT-PCR are used for diagnosis shows the possibility of nearly halving the time required for RT-PCR diagnosis.

Validation of the North America expert consensus statement on reporting CT findings for COVID-19 in individuals with lung cancer

The aim of our study was to validate the use of the standardized Radiological Society of North America (RSNA) reporting system in individuals with known lung cancer who presented to the emergency department with suspected COVID-19. We included patients aged 18 years or older from the Cancer Institute of the State of São Paulo (ICESP) with a confirmed diagnosis of lung cancer, admitted to the emergency department and undergoing chest computed tomography (CT) for suspicion of COVID-19. Comparison between SARS-CoV2 RT-PCR across RSNA categories was performed in all patients and further stratified by diagnosis of lung cancer progression. Among 58 individuals included in the analysis (65±9 years, 43% men), 20 had positive RT-PCR. Less than a half (43%) had no new lung findings in the CT. Positive RT-PCR was present in 75% of those with typical findings according to RSNA and in only 9% when these findings were classified as atypical or negative (P<0.001). Diagnostic accuracy was even higher when stratified by the presence or absence of progressive disease (PD). Extent of pulmonary inflammatory changes was strongly associated with higher mortality, reaching a lethality of 83% in patients with >25% of lung involvement and 100% when there was >50% of lung involvement. The lung involvement score was also highly predictive of prognosis in this population as was reported for non-lung cancer individuals. Collectively, our results demonstrated that diagnostic and prognostic values of chest CT findings in COVID-19 are robust to the presence of lung abnormalities related to lung cancer.

Label-free impedimetric immunosensor for point-of-care detection of COVID-19 antibodies

The COVID-19 pandemic has posed enormous challenges for existing diagnostic tools to detect and monitor pathogens. Therefore, there is a need to develop point-of-care (POC) devices to perform fast, accurate, and accessible diagnostic methods to detect infections and monitor immune responses. Devices most amenable to miniaturization and suitable for POC applications are biosensors based on electrochemical detection. We have developed an impedimetric immunosensor based on an interdigitated microelectrode array (IMA) to detect and monitor SARS-CoV-2 antibodies in human serum. Conjugation chemistry was applied to functionalize and covalently immobilize the spike protein (S-protein) of SARS-CoV-2 on the surface of the IMA to serve as the recognition layer and specifically bind anti-spike antibodies. Antibodies bound to the S-proteins in the recognition layer result in an increase in capacitance and a consequent change in the impedance of the system. The impedimetric immunosensor is label-free and uses non-Faradaic impedance with low nonperturbing AC voltage for detection. The sensitivity of a capacitive immunosensor can be enhanced by simply tuning the ionic strength of the sample solution. The device exhibits an LOD of 0.4 BAU/ml, as determined from the standard curve using WHO IS for anti-SARS-CoV-2 immunoglobulins; this LOD is similar to the corresponding LODs reported for all validated and established commercial assays, which range from 0.41 to 4.81 BAU/ml. The proof-of-concept biosensor has been demonstrated to detect anti-spike antibodies in sera from patients infected with COVID-19 within 1 h. Photolithographically microfabricated interdigitated microelectrode array sensor chips & label-free impedimetric detection of COVID-19 antibody.

The role of Lung Ultrasound in the diagnosis of SARS-COV-2 disease in pregnant women

AIM

To evaluate the role of lung ultrasound (LUS) in recognizing lung abnormalities in pregnant women affected by COVID-19 pneumonia.

MATERIALS AND METHODS

An observational study analyzing LUS patterns in 60 consecutively enrolled pregnant women affected by COVID-19 infection was performed. LUS was performed by using a standardized protocol by Soldati et al. The scoring system of LUS findings ranged from 0 to 3 in increasing alteration severity. The highest score obtained from each landmark was reported and the sum of the 12 zones examined was calculated.

RESULTS

Patients were divided into two groups: 26 (43.3%) patients with respiratory symptoms and 32 (53.3%) patients without respiratory symptoms; 2 patients were asymptomatic (3.3%). Among the patients with respiratory symptoms 3 (12.5%) had dyspnea that required a mild Oxygen therapy. A significant correlation was found between respiratory symptoms and LUS score (p < 0.001) and between gestational weeks and respiratory symptoms (p = 0.023). Regression analysis showed that age and respiratory symptoms were risk factors for highest LUS score (p < 0.005).

DISCUSSION

LUS can affect the clinical decision course and can help in stratifying patients according to its findings. The lack of ionizing radiation and its repeatability makes it a reliable diagnostic tool in the management of pregnant women.

A few-shot approach for COVID-19 screening in standard and portable chest X-ray images

Reliable and effective diagnostic systems are of vital importance for COVID-19, specifically for triage and screening procedures. In this work, a fully automatic diagnostic system based on chest X-ray images (CXR) has been proposed. It relies on the few-shot paradigm, which allows to work with small databases. Furthermore, three components have been added to improve the diagnosis performance: (1) a region proposal network which makes the system focus on the lungs; (2) a novel cost function which adds expert knowledge by giving specific penalties to each misdiagnosis; and (3) an ensembling procedure integrating multiple image comparisons to produce more reliable diagnoses. Moreover, the COVID-SC dataset has been introduced, comprising almost 1100 AnteroPosterior CXR images, namely 439 negative and 653 positive according to the RT-PCR test. Expert radiologists divided the negative images into three categories (normal lungs, COVID-related diseases, and other diseases) and the positive images into four severity levels. This entails the most complete COVID-19 dataset in terms of patient diversity. The proposed system has been compared with state-of-the-art methods in the COVIDGR-1.0 public database, achieving the highest accuracy (81.13% ± 2.76%) and the most robust results. An ablation study proved that each system component contributes to improve the overall performance. The procedure has also been validated on the COVID-SC dataset under different scenarios, with accuracies ranging from 70.81 to 87.40%. In conclusion, our proposal provides a good accuracy appropriate for the early detection of COVID-19.

Correlation between the initial CT chest findings and short-term prognosis in Egyptian patients with COVID-19 pneumonia

Background

The recent pandemic of COVID‐19 has thrown the world into chaos due to its high rate of transmissions. This study aimed to highlight the encountered CT findings in 910 patients with COVID-19 pneumonia in Egypt including the mean severity score and also correlation between the initial CT finding and the short-term prognosis in 320 patients.

Results

All patients had confirmed COVID-19 infection. Non-contrast CT chest was performed for all cases; in addition, the correlation between each CT finding and disease severity or the short-term prognosis was reported. The mean age was higher for patients with unfavorable prognosis (P < 0.01). The patchy pattern was the most common, found in 532/910 patients (58.4%), the nodular pattern was the least common 123/910 (13.5%). The diffuse pattern was reported in 124 (13.6%). The ground glass density was the most common reported density in the study 512/910 (56.2%). The crazy pavement sign was reported more frequently in patients required hospitalization or ICU and was reported in 53 (56.9%) of patients required hospitalization and in 29 (40.2%) patients needed ICU, and it was reported in 11 (39.2%) deceased patients. Air bronchogram was reported more frequently in patients with poor prognosis than patients with good prognosis (16/100; 26% Vs 12/220; 5.4%). The mean CT severity score for patients with poor prognosis was 15.2. The mean CT severity score for patients with good prognosis 8.7., with statistically significant difference (P = 0.001).

Conclusion

Our results confirm the important role of the initial CT findings in the prediction of clinical outcome and short-term prognosis. Some signs like subpleural lines, halo sign, reversed halo sign and nodular shape of the lesions predict mild disease and favorable prognosis. The crazy paving sign, dense vessel sign, consolidation, diffuse shape and high severity score predict more severe disease and probably warrant early hospitalization. The high severity score is most important in prediction of unfavorable prognosis. The nodular shape of the lesions is the most important predictor of good prognosis.

Agreement between low-dose and ultra-low-dose chest CT for the diagnosis of viral pneumonia imaging patterns during the COVID-19 pandemic

Background

Chest CT scan has an important role in the diagnosis and management of COVID-19 infection. A major concern in radiologic assessment of the patients is the radiation dose. Research has been done to evaluate low-dose chest CT in the diagnosis of pulmonary lesions with promising findings. We decided to determine diagnostic performance of ultra-low-dose chest CT in comparison to low-dose CT for viral pneumonia during the COVID-19 pandemic.

Results

167 patients underwent both low-dose and ultra-low-dose chest CT scans. Two radiologists blinded to the diagnosis independently examined ultra-low-dose chest CT scans for findings consistent with COVID-19 pneumonia. In case of any disagreement, a third senior radiologist made the final diagnosis. Agreement between two CT protocols regarding ground-glass opacity, consolidation, reticulation, and nodular infiltration were recorded. On low-dose chest CT, 44 patients had findings consistent with COVID-19 infection. Ultra-low-dose chest CT had sensitivity and specificity values of 100% and 98.4%, respectively for diagnosis of viral pneumonia. Two patients were falsely categorized to have pneumonia on ultra-low-dose CT scan. Positive predictive value and negative predictive value of ultra-low-dose CT scan were respectively 95.7% and 100%. There was good agreement between low-dose and ultra-low-dose methods (kappa = 0.97; P < 0.001). Perfect agreement between low-dose and ultra-low-dose scans was found regarding diagnosis of ground-glass opacity (kappa = 0.83, P < 0.001), consolidation (kappa = 0.88, P < 0.001), reticulation (kappa = 0.82, P < 0.001), and nodular infiltration (kappa = 0.87, P < 0.001).

Conclusion

Ultra-low-dose chest CT scan is comparable to low-dose chest CT for detection of lung infiltration during the COVID-19 outbreak while maintaining less radiation dose. It can also be used instead of low-dose chest CT scan for patient triage in circumstances where rapid-abundant PCR tests are not available.

Do COVID-19 CT features vary between patients from within and outside mainland China? Findings from a meta-analysis

BACKGROUND

Chest computerized tomography (CT) plays an important role in detecting patients with suspected coronavirus disease 2019 (COVID-19), however, there are no systematic summaries on whether the chest CT findings of patients within mainland China are applicable to those found in patients outside.

METHODS

Relevant studies were retrieved comprehensively by searching PubMed, Embase, and Cochrane Library databases before 15 April 2022. Quality assessment of diagnostic accuracy studies (QUADAS) was used to evaluate the quality of the included studies, which were divided into two groups according to whether they were in mainland China or outside. Data on diagnostic performance, unilateral or bilateral lung involvement, and typical chest CT imaging appearances were extracted, and then, meta-analyses were performed with R software to compare the CT features of COVID-19 pneumonia between patients from within and outside mainland China.

RESULTS

Of the 8,258 studies screened, 19 studies with 3,400 patients in mainland China and 14 studies with 554 outside mainland China were included. Overall, the risk of quality assessment and publication bias was low. The diagnostic value of chest CT is similar between patients from within and outside mainland China (93, 91%). The pooled incidence of unilateral lung involvement (15, 7%), the crazy-paving sign (31, 21%), mixed ground-glass opacities (GGO) and consolidations (51, 35%), air bronchogram (44, 25%), vascular engorgement (59, 33%), bronchial wall thickening (19, 12%), and septal thickening (39, 26%) in patients from mainland China were significantly higher than those from outside; however, the incidence rates of bilateral lung involvement (75, 84%), GGO (78, 87%), consolidations (45, 58%), nodules (12, 17%), and pleural effusion (9, 15%) were significantly lower.

CONCLUSION

Considering that the chest CT features of patients in mainland China may not reflect those of the patients abroad, radiologists and clinicians should be familiar with various CT presentations suggestive of COVID-19 in different regions.

Every cloud has a silver lining: COVID-19 chest-CT screening prevents unnecessary cardiac surgery

BACKGROUND

Unenhanced chest CT can identify incidental findings (IFs) leading to management strategy change. We report our institutional experience with routine chest-CT as preoperative screening tool during the COVID-19 pandemic, focusing on the impact of IFs.

METHODS

All patients scheduled for cardiac surgery from May 1st to December 31^st 2020, underwent preoperative unenhanced chest-CT according to COVID-19 pandemic institutional protocol. We have analyzed IFs incidence, reported consequent operative changes, and identified IFs clinical determinants.

RESULTS

Out of 447, 278 patients were included. IFs rate was 7.2% (20/278): a solid mass (11/20, 55%), lymphoproliferative disease (1/20, 5%), SARS-CoV-2 pneumonia (2/20, 10%), pulmonary artery chronic thromboembolism (1/20, 5%), anomalous vessel anatomy (2/20, 10%), voluminous hiatal hernia (1/20, 5%), mitral annulus calcification (1/20, 5%), and porcelain aorta (1/20, 5%) were reported. Based on IFs, 4 patients (20%-4/278, 1.4%) were not operated, 8 (40%-8/278, 2.9%) underwent a procedure different from the one originally planned one, and 8 (40%-8/278, 2.9%) needed additional preoperative investigations before undergoing the planned surgery. At univariate regression, coronary artery disease, atrial fibrillation, and history of cancer were significantly more often present in patients presenting with significant IFs. History of malignancy was identified as the only independent determinant of significant IFs at chest-CT (OR=4.27 IQR: [1.14-14.58], P=0.0227).

CONCLUSIONS

Unenhanced chest-CT as a preoperative screening tool in cardiac surgery led to incidental detection of significant clinical findings, which justified even procedures cancellation. Malignancy history is a determinant for CT incidental findings and could support a tailored screening approach for high-risk patients.

Optimisation of COVID‐19 diagnostic pathways in acute hospital admissions to prevent nosocomial transmission

Introduction

In the management of acute hospital admissions during the COVID‐19 pandemic, safe patient cohorting depends on robust admission diagnostic strategies. It is essential that screening strategies are sensitive and rapid, to prevent nosocomial transmission of COVID‐19 and maintain patient flow.

Methods

We retrospectively identified all COVID‐19 positive and suspected cases at our institution screened by reverse transcription polymerase chain reaction (RT‐PCR) between 4 April and 28 June 2020. Using RT‐PCR positivity within 7 days as our reference standard, we assessed sensitivity and net‐benefit of three admission screening strategies: single admission RT‐PCR, composite admission RT‐PCR and CXR and repeat RT‐PCR with 48 h.

Results

RT‐PCR single‐test sensitivity was 91.5% (87.8%–94.4%) versus 97.7% (95.4%–99.1%) (p = 0.025) for RT‐PCR/CXR composite testing and 95.1% (92.1%–97.2%) (p = 0.03) for repeated RT‐PCR. Net‐benefit was 0.83 for single RT‐PCR versus 0.89 for RT‐PCR/CXR and 0.87 for repeated RT‐PCR at 0.02% threshold probability.

Conclusion

The RT‐PCR/CXR composite testing strategy was highly sensitive when screening patients at the point of hospital admission. Real‐world sensitivity of this approach was comparable to repeat RT‐PCR testing within 48 h; however, faster facilitating improved patient flow.

Molecular imaging as a tool for evaluation of COVID-19 sequelae – A review of literature

Coronavirus disease 2019 (COVID-19) is caused by the novel viral pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 primarily involves the lungs. Nucleic acid testing based on reverse-transcription polymerase chain reaction of respiratory samples is the current gold standard for the diagnosis of SARS-CoV-2 infection. Imaging modalities have an established role in triaging, diagnosis, evaluation of disease severity, monitoring disease progression, extra-pulmonary involvement, and complications. As our understanding of the disease improves, there has been substantial evidence to highlight its potential for multi-systemic involvement and development of long-term sequelae. Molecular imaging techniques are highly sensitive, allowing non-invasive visualization of physiological or pathological processes at a cellular or molecular level with potential for detection of functional changes earlier than conventional radiological imaging. The purpose of this review article is to highlight the evolving role of molecular imaging in evaluation of COVID-19 sequelae. Though not ideal for diagnosis, the various modalities of molecular imaging play an important role in assessing pulmonary and extra-pulmonary sequelae of COVID-19. Perfusion imaging using single photon emission computed tomography fused with computed tomography (CT) can be utilized as a first-line imaging modality for COVID-19 related pulmonary embolism. ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/CT is a sensitive tool to detect multi-systemic inflammation, including myocardial and vascular inflammation. PET in conjunction with magnetic resonance imaging helps in better characterization of neurological sequelae of COVID-19. Despite the fact that the majority of published literature is retrospective in nature with limited sample sizes, it is clear that molecular imaging provides additional valuable information (complimentary to anatomical imaging) with semi-quantitative or quantitative parameters to define inflammatory burden and can be used to guide therapeutic strategies and assess response. However, widespread clinical applicability remains a challenge owing to longer image acquisition times and the need for adoption of infection control protocols.

An Umbrella Review With Meta-Analysis of Chest Computed Tomography for Diagnosis of COVID-19: Considerations for Trauma Patient Management

Background

RT-PCR testing is the standard for diagnosis of COVID-19, although it has its suboptimal sensitivity. Chest computed tomography (CT) has been proposed as an additional tool with diagnostic value, and several reports from primary and secondary studies that assessed its diagnostic accuracy are already available. To inform recommendations and practice regarding the use of chest CT in the in the trauma setting, we sought to identify, appraise, and summarize the available evidence on the diagnostic accuracy of chest CT for diagnosis of COVID-19, and its application in emergency trauma surgery patients; overcoming limitations of previous reports regarding chest CT accuracy and discussing important considerations regarding its role in this setting.

Methods

We conducted an umbrella review using Living Overview of Evidence platform for COVID-19, which performs regular automated searches in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and more than 30 other sources. The review was conducted following the JBI methodology for systematic reviews. The Grading of Recommendations, Assessment, Development, and Evaluation approach for grading the certainty of the evidence is reported (registered in International Prospective Register of Systematic Reviews, CRD42020198267).

Results

Thirty studies that fulfilled selection criteria were included; 19 primary studies provided estimates of sensitivity (0.91, 95%CI = [0.88-0.93]) and specificity (0.73, 95%CI = [0.61; 0.82]) of chest CT for COVID-19. No correlation was found between sensitivities and specificities (ρ = 0.22, IC95% [-0.33; 0.66]). Diagnostic odds ratio was estimated at: DOR = 27.5, 95%CI (14.7; 48.5). Evidence for sensitivity estimates was graded as MODERATE, and for specificity estimates it was graded as LOW.

Conclusion

The value of chest CT appears to be that of an additional screening tool that can easily detect PCR false negatives, which are reportedly highly frequent. Upon the absence of PCR testing and impossibility to perform RT-PCR in trauma patients, chest CT can serve as a substitute with increased value and easy implementation.

Systematic Review Registration

[www.crd.york.ac.uk/prospero], identifier [CRD42020198267].

Evidence of previous SARS-CoV-2 infection in seronegative patients with long COVID

BACKGROUND

There is currently no consensus on the diagnosis, definition, symptoms, or duration of COVID-19 illness. The diagnostic complexity of Long COVID is compounded in many patients who were or might have been infected with SARS-CoV-2 but not tested during the acute illness and/or are SARS-CoV-2 antibody negative.

METHODS

Given the diagnostic conundrum of Long COVID, we set out to investigate SARS-CoV-2-specific T cell responses in patients with confirmed SARS-CoV-2 infection and/or Long COVID from a cohort of mostly non-hospitalised patients.

FINDINGS

We discovered that IL-2 release (but not IFN-γ release) from T cells in response to SARS-CoV-2 peptides is both sensitive (75% +/-13%) and specific (88%+/-7%) for previous SARS-CoV-2 infection >6 months after a positive PCR test. We identified that 42-53% of patients with Long COVID, but without detectable SARS-CoV-2 antibodies, nonetheless have detectable SARS-CoV-2 specific T cell responses.

INTERPRETATION

Our study reveals evidence (detectable T cell mediated IL-2 release) of previous SARS-CoV-2 infection in seronegative patients with Long COVID.

FUNDING

This work was funded by the Addenbrooke's Charitable Trust (900276 to NS), NIHR award (G112259 to NS) and supported by the NIHR Cambridge Biomedical Research Centre. NJM is supported by the MRC (TSF MR/T032413/1) and NHSBT (WPA15-02). PJL is supported by the Wellcome Trust (PRF 210688/Z/18/Z, 084957/Z/08/Z), a Medical Research Council research grant MR/V011561/1 and the United Kingdom Research and a Innovation COVID Immunology Consortium grant (MR/V028448/1).

Clinical significance of chest CT for the exclusion of COVID-19 in pre-admission screening: Is it worthwhile using chest CT with reverse-transcription polymerase chain reaction test?

BACKGROUND

A single reverse-transcription polymerase chain reaction (RT-PCR) test is not sufficient to exclude COVID-19 in hospital pre-admission screening. However, repeated RT-PCR tests are time-consuming. This study investigates the utility of chest computed tomography (CT) for COVID-19 screening in asymptomatic patients.

METHODS

Between April 2020 and March 2021, RT-PCR testing and chest CT were performed to screen COVID-19 in 10 823 asymptomatic patients prior to admission. Chest CT findings were retrospectively evaluated using the reporting system of the Radiological Society of North America. Using RT-PCR results as a reference, we assessed the diagnostic efficacy of chest CT during both the low- and high-prevalence periods of the COVID-19 pandemic.

RESULTS

Following a positive RT-PCR test, 20 asymptomatic patients (0.18%) were diagnosed with COVID-19; in the low-prevalence period, 5 of 6556 patients (0.076%) were positive; and in the high-prevalence period, 15 of 4267 patients (0.35%) were positive. Of the 20 asymptomatic COVID-19 positive patients, chest CT results were positive for COVID-19 pneumonia in 8 patients. Chest CT results were false-positive in 185 patients (1.7% false-positive rate, and 60% false-negative rate). Pneumonia that was classified as a "typical appearance" of COVID-19 reported as false-positives in 36 of 39 patients (92.3%). Across the study period, the diagnostic efficacy of "typical appearance" on chest CT were characterized by a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 15%, 99.7%, 99.7%, 7.7%, and 99.8%; 20%, 99.6%, 99.6%, 4%, and 99.9%; and 13.3%, 99.7%, 99.7%, 14.3%, and 99.7%, in the entire study, low-, and high-prevalence periods, respectively.

CONCLUSIONS

Addition of chest CT to RT-PCR testing provides no benefit to the detection of COVID-19 in asymptomatic patients.

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT

With coronavirus disease 2019 (COVID-19) cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on chest computed tomography (CT) scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 chest CT images from the dataset entitled "A COVID multiclass dataset of CT scans," with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769 ± 0.0046, accuracy of 0.9759 ± 0.0048, sensitivity of 0.9788 ± 0.0055, specificity of 0.9730 ± 0.0057, and precision of 0.9751 ± 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845 ± 0.0109, F1 score of 0.9599 ± 0.0251, sensitivity of 0.9682 ± 0.0099, specificity of 0.9883 ± 0.0150, and precision of 0.9526 ± 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 s on GPUs and 0.5 s on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.

Chest CT Characteristics are Strongly Predictive of Mortality in Patients with COVID-19 Pneumonia: A Multicentric Cohort Study

Rationale and Objectives

The novel coronavirus (COVID-19) has presented a significant and urgent threat to global health and there has been a need to identify prognostic factors in COVID-19 patients. The aim of this study was to determine whether chest computed tomography (CT) characteristics had any prognostic value in patients with COVID-19.

Materials and Methods

A retrospective analysis of COVID-19 patients who underwent a chest CT-scan was performed in four medical centers. The prognostic value of chest CT results was assessed using a multivariable survival analysis with the Cox model. The characteristics included in the model were the degree of lung involvement, ground glass opacities, nodular consolidations, linear consolidations, a peripheral topography, a predominantly inferior lung involvement, pleural effusion, and crazy paving. The model was also adjusted on age, sex, and the center in which the patient was hospitalized. The primary endpoint was 30-day in-hospital mortality. A second model used a composite endpoint of admission to an intensive care unit or 30-day in-hospital mortality.

Results

A total of 515 patients with available follow-up information were included. Advanced age, a degree of pulmonary involvement ≥50% (Hazard Ratio 2.25 [95% CI: 1.378-3.671], p = 0.001), nodular consolidations and pleural effusions were associated with lower 30-day in-hospital survival rates. An exploratory subgroup analysis showed a 60.6% mortality rate in patients over 75 with ≥50% lung involvement on a CT-scan.

Conclusion

Chest CT findings such as the percentage of pulmonary involvement ≥50%, pleural effusion and nodular consolidation were strongly associated with 30-day mortality in COVID-19 patients. CT examinations are essential for the assessment of severe COVID-19 patients and their results must be considered when making care management decisions.

Exploring the Deep-Learning Techniques in Detecting the Presence of Coronavirus in the Chest X-Ray Images: A Comprehensive Review

The deadly coronavirus (COVID-19) is one of the dangerous diseases affecting the entire world and is fastly spreading disease. This spread can be reduced by detecting and quarantining the patients at an earlier stage. The most common diagnostic tool for detecting the coronavirus is the Reverse transcription-polymerase chain reaction (RT-PCR) test which is time-consuming and also needs more equipment and manpower. Furthermore, many countries had a deficit of RTPCR kits. This is why it is exceptionally very crucial to develop artificial intelligence (AI) techniques to detect the outbreak of coronavirus. This motivated many researchers to involve deep-learning methods using X-ray images for more decisive analysis. Thus, this paper outlines many papers that used traditional and pre-trained deep learning methods that are newly developed to reduce the spread of COVID-19 disease. Specifically, advanced deep learning methods play a critical role in extracting the features from the chest X-ray images. These features are then used to classify whether the patient is affected with coronavirus or not. Besides, this paper shows that deep learning techniques have probable applications in the medical field.

CT Findings in Patients with COVID-19-Compatible Symptoms but Initially Negative qPCR Test

PURPOSE

 To assess whether it is possible to reliably detect patients with strong suspicion of COVID-19 despite initially negative quantitative polymerase-chain-reaction (qPCR) tests by means of computed tomography (CT).

MATERIALS AND METHODS

 437 patients with suspected COVID-19 but initially negative qPCR and subsequent chest CT between March 13 and November 30, 2020 were included in this retrospective study. CT findings were compared to results of successive qPCR tests (minimum of 3 qPCR tests if CT suggested infection) to determine the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of CT for diagnosing COVID-19.

RESULTS

 COVID-19 was diagnosed correctly with a sensitivity of 100 % [95 % confidence interval (CI): 65-100] and a specificity of 88 % [95 % CI: 84-90]. A PPV of 12 % [95 % CI: 6-22] and an NPV of 100 % [95 % CI: 99-100] were determined.

CONCLUSION

 CT is able to detect COVID-19 before qPCR in initially negative patients in this special study setting. Similar CT findings in COVID-19 and other atypical pneumonias can lead to high numbers of false-positive patients, reducing the specificity of CT.

KEY POINTS

  · Low-dose chest CT is able to diagnose COVID-19 in symptomatic patients even in cases of an initially negative quantitative PCR result and therefore is a fast support method to detect COVID-19, especially in early disease.. · Low-dose chest CT can reliably exclude COVID-19 in a pandemic setting.. · CT does not always ensure a reliable differentiation from other viral diseases..

CITATION FORMAT

· Valentin B, Steuwe A, Wienemann T, et al. CT Findings in Patients with COVID-19-Compatible Symptoms but Initially Negative qPCR Test. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1779-9230.

Commercial AI solutions in detecting COVID-19 pneumonia in chest CT: not yet ready for clinical implementation?

OBJECTIVES

In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions.

METHODS

Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs).

RESULTS

Sensitivity and specificity ranges were 62-96% and 31-80%, respectively. Negative and positive predictive values ranged between 82-99% and 19-25%, respectively. AUC was in the range 0.54-0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54-0.69.

CONCLUSIONS

This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis.

KEY POINTS

• Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made.

Analysis of the role of PMCT during the COVID-19 pandemic: a systematic review

Objectives

During COVID-19 pandemic PMCT has been proposed as a forensic investigation method. This systematic review is aimed to systematize evidence and peer-reviewed opinions reported during the first two years of pandemic, to evaluate the role of PMCT during the COVID-19 pandemic.

Materials and methods

An online literature search was performed to identify publications on PMCT during the COVID-19 pandemic between December 2019 and March 2022. For each publication included, the following data were collected: title and abstract, year of publication, type of article, number and type of individuals examined. The selected publications were also categorized based on PMCT findings, histopathological results, the comparison between PMCT and histopathological findings, cause of death and proposed role of PMCT during the pandemic.

Results

A total of 20 publications were included, mostly case reports (9/20). All cases examined included adults. The most frequent PMCT pattern in positive cases was diffuse mixed densities with prevalence of consolidations (pattern 1) (54%). In 97% of the cases where a comparison between PMCT and histological results was performed, a correspondence was found. In 82% of the cases the principal cause of death was COVID-19 pneumonia. PMCT has been proposed as a pre-autopsy screening tool in 62%, and as a method for augmenting post-mortem data in 50% of the papers reporting this issue.

Conclusion

This systematic review suggests that PMCT should be regarded as a highly valuable investigative technique for the forensic evaluation of deaths with ascertained or suspected COVID-19 pneumonia.

Low dose Lung-CT as COVID-19 diagnostic tool while waiting for RT-PCR result

Reverse transcription‐polymerase chain reaction (RT‐PCR) assay as the standard of COVID19 diagnosis takes time, can only be done in limited laboratories, and sometimes produced false negative result; which made diagnosis and intervention become delayed. Meanwhile, chest CT takes little time and has high sensitivity in diagnosing COVID19. This study aimed to see the correlation of Chest CT with RT-PCR results in COVID19 patients.  We performed a retrospective cohort study from symptomatic patients at Pantai Indah Kapuk Hospital in Jakarta, Indonesia. Multi-detector scanner CT was done, RT-PCR samples were taken and sent to the government appointed laboratory. Main outcome measures include correlation of CT patterns and CT severity index with RT-PCR. Data were processed with SPSS ver. 25.0 using gamma coefficient measure of agreement. Seventy-three patients were included and underwent chest CT and compared the result with RT-PCR. This study showed the very strong correlation (Gamma +0.897, p-value <0.05, CI 95%) between CT pattern with RT-PCR and no correlation (Gamma +0.241, p-value = 0.379, CI 95%) between CT severity index with RT-PCR. Chest CT has proven its superiority to be used as one of the most capable diagnostic devices for COVID19 patients. 

COVID-CXNet: Detecting COVID-19 in frontal chest X-ray images using deep learning

One of the primary clinical observations for screening the novel coronavirus is capturing a chest x-ray image. In most patients, a chest x-ray contains abnormalities, such as consolidation, resulting from COVID-19 viral pneumonia. In this study, research is conducted on efficiently detecting imaging features of this type of pneumonia using deep convolutional neural networks in a large dataset. It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making. In this paper, numerous chest x-ray images from several sources are collected, and one of the largest publicly accessible datasets is prepared. Finally, using the transfer learning paradigm, the well-known CheXNet model is utilized to develop COVID-CXNet. This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization. COVID-CXNet is a step towards a fully automated and robust COVID-19 detection system.