Role of Sonography in COVID-19 Lung Imaging

The article discusses the key role played by sonography in conducting lung imaging to patients infected with Covid-19. Also cited are the use of computed tomography (CT) imaging of the chest to diagnose and manage patients with suspected or confirmed coronavirus infection, and the benefits of sonography like its lack of ionizing radiation and low price and maintenance costs.

COVID-19 and vaccination impact on radiology departments provided by Dose Archiving and Communication System.

OBJECTIVES: To study the impact of COVID-19 on chest CT practice during the different waves using Dose Archiving and Communication System (DACS). METHODS: Retrospective study including data from 86,136 chest CT acquisitions from 27 radiology centers (15 private; 12 public) between January 1, 2020, and October 13, 2021, using a centralized DACS. Daily chest CT activity and dosimetry information such as dose length product (DLP), computed tomography dose index (CTDI), and acquisition parameters were collected. Pandemic indicators (daily tests performed, incidence, and hospital admissions) and vaccination rates were collected from a governmental open-data platform. Descriptive statistics and correlation analysis were performed. RESULTS: For the first two waves, strong positive and significant correlations were found between all pandemic indicators and total chest CT activity, as high as R = 0.7984 between daily chest CT activity and hospital admissions during the second wave (p < 0.0001). We found differences between public hospitals and private imaging centers during the first wave, with private centers demonstrating a negative correlation between daily chest CT activity and hospital admissions (-0.2819, p = 0.0019). Throughout the third wave, simultaneously with the rise of vaccination rates, total chest CT activity decreased with significant negative correlations with pandemic indicators, such as R = -0.7939 between daily chest CTs and daily incidence (p < 0.0001). Finally, less than 5% of all analyzed chest CTs could be considered as low dose. CONCLUSIONS: During the first waves, COVID-19 had a strong impact on chest CT practice which was lost with the arrival of vaccination. Low-dose protocols remained marginal. KEY POINTS: • There was a significant correlation between the number of daily chest CTs and pandemic indicators throughout the first two waves. It was lost during the third wave due to vaccination arrival. • Differences were observed between public and private centers, especially during the first wave, less during the second, and were lost during the third. • During the first three waves of COVID-19 pandemic, few CT helical acquisitions could be considered as low dose with only 3.8% of the acquisitions according to CTDIvol and 4.3% according to DLP.

Role of Sonography in COVID-19 Lung Imaging

The article discusses the key role played by sonography in conducting lung imaging to patients infected with Covid-19. Also cited are the use of computed tomography (CT) imaging of the chest to diagnose and manage patients with suspected or confirmed coronavirus infection, and the benefits of sonography like its lack of ionizing radiation and low price and maintenance costs.

Utilizing Clustering FCM And Contrast Based Enhancement to Isolate Covid-19 Infected Regions in CT Lung Images

Covid-19 disease that directly affecting lungs is an acute disease caused death of many people around word, so the early detecting of it and asses the relative ratio of the lung infection is a vital need. In this work, Histogram based contrast adjustment was implemented to enhance four lung abnormal CT scan images to highlight the abnormal regions within the experimental images. Fuzzy c-mean algorithm then was applied to segment the images in order to detect and isolate the infected regions. Besides, several morphological operations were employed to extract the refined infected Covid-19 areas effectively with accuracy of 96%.

Detection Of Covid19 in Lung X-Rays Using Deep Learning Algorithms

The World Health Organization (WHO) compiled this medical imaging reference guide in response to the emergence of the COVID-19 virus. The Beijing Country Office of the World Health Organization learned on December 31 that there was an epidemic of pneumonia patients in Wuhan, China. The causative agent of the pandemic was quickly identified as a novel coronavirus. In 2019, we should anticipate seeing an increase in the prevalence of coronavirus sickness, also known as the SARS-CoV-2 virus, and the SARS-CoV-1 virus. In order to determine the presence of this virus (COVID-19), we have created two models. Finally, the distorted part of the image was located. Some of the processes that we go through regularly have been the subject of our efforts to automate them. Using Resnet-18 models in combination with Deep Convolutional Neural Network (DenseNet-121 & Resnet-18) models, we were able to successfully detect COVID-19. The Densenet-121 model did well in its training and evaluation on a dataset of 1600 chest X-ray images. Over 2700 CXR pictures may be used for model training and evaluation with Resnet18. We have separated the data into groups according to the suggested models and found widely varying degrees of precision across the board. Data from both sources showed that Densenet-121 was the most reliable model.

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.

Interleukin 18 and IL-18 BP response to Sars-CoV-2 virus infection.

The immune response to the SARS-CoV-2 infection is crucial to the patient outcome. IL-18 is involved in the lymphocyte response to the disease and it is well established its important role in the complex developing of the host response to viral infection. This study aims at the analysis of the concentrations of IL-18, IL-18BP, INF-γ at the onset of the SARS-CoV-2 infection. The serum levels of measured interleukins were obtained through enzyme-linked immunosorbent assay. Furthermore, the free fraction of IL-18 was numerically evaluated. The enrolled patients were divided in two severity groups according to a threshold value of 300 for the ratio of arterial partial pressure of oxygen and fraction of inspired oxygen fraction and according to the parenchymal involvement as evaluated by computerized tomography at the admittance. In the group of patients with a more severe disease, a significant increase of the IL-18, INF-γ and IL-18BP levels have been observed, whereas the free IL-18 component values were almost constant. The results confirm that, at the onset of the disease, the host response keep the inflammatory cytokines in an equilibrium and support the hypothesis to adopt the IL-18BP modulation as a possible and effective therapeutic approach.

Mediastinal Emphysema, Giant Bulla, and Pneumothorax Developed during the Course of COVID-19 Pneumonia.

The coronavirus disease 2019 (COVID-19) pneumonia is a recent outbreak in mainland China and has rapidly spread to multiple countries worldwide. Pulmonary parenchymal opacities are often observed during chest radiography. Currently, few cases have reported the complications of severe COVID-19 pneumonia. We report a case where serial follow-up chest computed tomography revealed progression of pulmonary lesions into confluent bilateral consolidation with lower lung predominance, thereby confirming COVID-19 pneumonia. Furthermore, complications such as mediastinal emphysema, giant bulla, and pneumothorax were also observed during the course of the disease.

2019 Novel Coronavirus (COVID-19) Pneumonia: Serial Computed Tomography Findings.

From December 2019, Coronavirus disease 2019 (COVID-19) pneumonia (formerly known as the 2019 novel Coronavirus [2019-nCoV]) broke out in Wuhan, China. In this study, we present serial CT findings in a 40-year-old female patient with COVID-19 pneumonia who presented with the symptoms of fever, chest tightness, and fatigue. She was diagnosed with COVID-19 infection confirmed by real-time reverse-transcriptase-polymerase chain reaction. CT showed rapidly progressing peripheral consolidations and ground-glass opacities in both lungs. After treatment, the lesions were shown to be almost absorbed leaving the fibrous lesions.

Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.

OBJECTIVE: To develop an automatic COVID-19 Reporting and Data System (CO-RADS)-based classification in a multi-demographic setting. METHODS: This multi-institutional review boards-approved retrospective study included 2720 chest CT scans (mean age, 58 years [range 18-100 years]) from Italian and Russian patients. Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS-based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography. RESULTS: The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (k = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the "wavelet_(LH)_GLCM_Imc1" feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types. CONCLUSION: Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment. KEYPOINTS: • Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 ± 0.04. • Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration. • Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92.

Concomitant COVID-19 and pulmonary tuberculosis: computed tomography aspects.

OBJECTIVE: To describe the relationship between coronavirus disease 2019 (COVID-19) and pulmonary tuberculosis during the current pandemic, as well as to describe the main computed tomography (CT) findings in patients suffering from both diseases simultaneously. MATERIALS AND METHODS: This was a retrospective, cross-sectional observational study of the chest CT scans of 360 patients with COVID-19, as confirmed by RT-PCR. RESULTS: In four (1.1%) of the patients, changes suggestive of COVID-19 and tuberculosis were observed on the initial CT scan of the chest. On chest CT scans performed for the follow-up of COVID-19, cavitary lesions with bronchogenic spread were observed in two of the four patients, whereas alterations consistent with the progression of fibrous scarring related to previous tuberculosis were observed in the two other patients. The diagnosis of tuberculosis was confirmed by the isolation of Mycobacterium tuberculosis. CONCLUSION: Albeit rare, concomitant COVID-19 and tuberculosis can be suggested on the basis of the CT aspects. Radiologists should be aware of this possibility, because initial studies indicate that mortality rates are higher in patients suffering from both diseases simultaneously.

OBJETIVO: Descrever a associação entre COVID-19 e tuberculose pulmonar durante a pandemia atual e descrever os principais achados tomográficos. MATERIAIS E MÉTODOS: Estudo retrospectivo transversal e observacional de tomografias computadorizadas de tórax realizadas em 360 pacientes com COVID-19 confirmada por RT-PCR. RESULTADOS: Em quatro pacientes (1,1%) foram encontradas alterações tomográficas sugestivas de associação entre COVID-19 e tuberculose. Em dois pacientes observaram-se escavações com disseminação broncogênica e em outros dois, alterações compatíveis com progressão de lesões fibrocicatriciais relacionadas a tuberculose prévia, em exames de controle para COVID-19. O diagnóstico foi confirmado pelo isolamento do Mycobacterium tuberculosis. CONCLUSÃO: Apesar de incomum, a associação entre COVID-19 e tuberculose pode ser sugerida com base em aspectos tomográficos, devendo os radiologistas estar atentos a esta possibilidade, pois estudos iniciais indicam aumento da mortalidade nesses pacientes.

Initial comparative analysis of pulmonary involvement on HRCT between vaccinated and non-vaccinated subjects of COVID-19.

OBJECTIVES: To compare the high-resolution computed tomography (HRCT)-derived severity score in COVID-19 patients between those who had earlier received the vaccine against the SARS-CoV-2 and those who did not. METHODS: A retrospective cross-sectional analysis of HRCT of the chest was done in correlation with the vaccination status of clinically diagnosed COVID-19 patients. The variable under evaluation was the CT severity score, whereby differential analysis of the variability on this parameter between incompletely (single dose) vaccinated, completely (both doses) vaccinated, and non-vaccinated individuals was the outcome. RESULTS: The analysis included 826 patients of which 581 did not receive any vaccination whereas 196 patients received incomplete (single dose) vaccination and 49 received complete vaccination. Mean CT severity score was lower in completely vaccinated patients (3.5 ± 6.3) vis-à-vis incompletely vaccinated (10.1 ± 10.5) and non-vaccinated (10.1 ± 11.4) individuals. The mean CT score was significantly lower in completely vaccinated patients of lower ages (≤ 60 years) compared to patients above that age. The incidence of severe disease (CT score ≥ 20) was significantly higher in the incompletely vaccinated and non-vaccinated patients compared to that in the completely vaccinated group. CONCLUSIONS: CT severity scores in individuals receiving both doses of SARS-CoV-2 vaccination were less severe in comparison to those receiving a single dose of vaccine or no vaccine at all. KEY POINTS: • Patients who received complete two doses of vaccination had significantly low mean CT scores compared to the partially vaccinated patients and non-vaccinated patients. • The mean CT scores were significantly lower in completely vaccinated patients of lower ages (< 60 years) while patients > 60 years did not show significantly different CT scores between the vaccinated and non-vaccinated groups. • Consolidations and ground-glass opacities were significantly lower in the group receiving complete vaccination as compared to the unvaccinated and incompletely vaccinated patients.

Correlation between HRCT Findings of Symptomatic COVID-19 Patient with RT-PCR Test in a Tertiary Hospital of Bangladesh

The COVID-19 pandemic has turned into a global calamity and affected millions of lives around the world. Even though the vaccination efforts have started, they are yet to have an effective impact on the lower to middle-income countries. Early detection and isolation are still the best way to control the spread of the virus. The standard practice for detecting COVID-19 is the RT-PCR (Reverse Transcription Polymerase Chain Reaction) test but this test has a high probability of producing false-negative results plus lack of availability at all the time due to shortage of kit. Since COVID-19 is a respiratory disease affecting the lungs and the imaging patterns caused by COVID-19 can be observed in chest HRCT (High Resolution Computed Tomography) scans. As a result, HRCT can be used as an alternative diagnostic modality for any suspected cases of COVID-19. In this cross-sectional study was carried out in the Department of Radiology and Imaging, BSMMU, Dhaka, Bangladesh from May 12, 2020 to August 10, 2020. Chest HRCT scans of 284 suspected patients irrespective of age and sex who had done RT-PCR test either positive or negative test result having symptoms suggesting COVID pneumonia were enrolled in this study. Patients who had not done RT-PCR and who were not willing to do HRCT chest were excluded. According to the study, ground glass opacity is the most common feature and found in 89.44% of patients. The other predominant features were including consolidation, crazy paving, fibrotic density and vascular enlargement. The diagnostic performance of the CT scan was also evaluated using the RT-PCR test result as the gold standard and the sensitivity, specificity, and accuracy of the CT scan diagnosis were found to be 83.2%, 50% and 79.9% respectively. The severity of the five lung lobes has also been studied. The right middle lobe and the left upper lobe seemed to be in more severe condition for most of the patients compared to the other lung lobes.

Compressive ultrasound can predict early pulmonary embolism onset in COVID patients.

PURPOSE: To evaluate the usefulness of compressive ultrasound (CUS) for the diagnosis of deep vein thrombosis (DVT) in patients with SARS-CoV-2-related infection. METHODS: 112 hospitalized patients with confirmed SARS-CoV-2 infection were retrospectively enrolled. CUS was performed within 2 days of admission and consisted in the assessment of the proximal and distal deep venous systems. Lack of compressibility, or direct identification of an endoluminal thrombus, were the criteria used for the diagnosis of DVT. Pulmonary embolism (PE) events were investigated at computed tomography pulmonary angiography (CTPA) within 5 days of follow-up. Logistic binary regression was computed to determine which clinical and radiological parameters were independently associated with PE onset. RESULTS: Overall, the incidence of DVT in our cohort was about 43%. The most common district involved was the left lower limb (68.7%) in comparison with the right one (58.3%) while the upper limbs were less frequently involved (4.2% the right one and 2.1% the left one, respectively). On both sides, the distal tract of the popliteal vein was the most common involved (50% right side and 45.8% left side). The presence of DVT in the distal tract of the right popliteal vein (OR = 2.444 95%CIs 1.084-16.624, p = 0.038), in the distal tract of the left popliteal vein (OR = 4.201 95%CIs 1.484-11.885, p = 0.007), and D-dimer values (OR = 2.122 95%CIs 1.030-5.495, p = 0.003) were independently associated with the onset on PE within 5 days. CONCLUSIONS: CUS should be considered a useful tool to discriminate which category of patients can develop PE within 5 days from admission.

Covid-19 respiratory sequelae : Screening and management

Infection with SARS-CoV-2 can affect multiple organ systems with variable severity and is known to frequently have a major impact on the respiratory system. Symptoms may persist for several months after infection, and are associated with a reduction of lung function, diminished exercise capacity and anomalies on chest CT. Guidelines on the post-acute care of patients with SARS-CoV-2 are now available. Pulmonary rehabilitation plays a central role in the recovery of exercise capacity, notably in severe cases. The role of specific therapies, such as corticosteroids, anti-fibrotics and lung transplantation remains uncertain and needs to be evaluated on a case-by-case basis. L’infection à SARS-CoV-2 conduit à une atteinte multisystémique de gravité variable, fréquemment avec un impact majeur sur le système respiratoire. Les symptômes peuvent persister plusieurs mois après l’infection initiale. Ils sont parfois associés à une diminution des fonctions pulmonaires et de la capacité à l’effort, ainsi qu’à des anomalies au CT-scan thoracique. Il existe actuellement des recommandations de suivi et de prise en charge des patients atteints par le Covid-19 pour la phase postaiguë. La réhabilitation pulmonaire joue un rôle central dans la récupération de la capacité d’effort, surtout dans les formes sévères. La place des traitements spécifiques des atteintes pulmonaires, notamment les corticostéroïdes, les antifibrotiques et la transplantation, reste encore incertaine et doit être considérée au cas par cas.