Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19.

Background Radiographic severity may help predict patient deterioration and outcomes from COVID-19 pneumonia. Purpose To assess the reliability and reproducibility of three chest radiograph reporting systems (radiographic assessment of lung edema [RALE], Brixia, and percentage opacification) in patients with proven SARS-CoV-2 infection and examine the ability of these scores to predict adverse outcomes both alone and in conjunction with two clinical scoring systems, National Early Warning Score 2 (NEWS2) and International Severe Acute Respiratory and Emerging Infection Consortium: Coronavirus Clinical Characterization Consortium (ISARIC-4C) mortality. Materials and Methods This retrospective cohort study used routinely collected clinical data of patients with polymerase chain reaction-positive SARS-CoV-2 infection admitted to a single center from February 2020 through July 2020. Initial chest radiographs were scored for RALE, Brixia, and percentage opacification by one of three radiologists. Intra- and interreader agreement were assessed with intraclass correlation coefficients. The rate of admission to the intensive care unit (ICU) or death up to 60 days after scored chest radiograph was estimated. NEWS2 and ISARIC-4C mortality at hospital admission were calculated. Daily risk for admission to ICU or death was modeled with Cox proportional hazards models that incorporated the chest radiograph scores adjusted for NEWS2 or ISARIC-4C mortality. Results Admission chest radiographs of 50 patients (mean age, 74 years ± 16 [standard deviation]; 28 men) were scored by all three radiologists, with good interreader reliability for all scores, as follows: intraclass correlation coefficients were 0.87 for RALE (95% CI: 0.80, 0.92), 0.86 for Brixia (95% CI: 0.76, 0.92), and 0.72 for percentage opacification (95% CI: 0.48, 0.85). Of 751 patients with a chest radiograph, those with greater than 75% opacification had a median time to ICU admission or death of just 1-2 days. Among 628 patients for whom data were available (median age, 76 years [interquartile range, 61-84 years]; 344 men), opacification of 51%-75% increased risk for ICU admission or death by twofold (hazard ratio, 2.2; 95% CI: 1.6, 2.8), and opacification greater than 75% increased ICU risk by fourfold (hazard ratio, 4.0; 95% CI: 3.4, 4.7) compared with opacification of 0%-25%, when adjusted for NEWS2 score. Conclusion Brixia, radiographic assessment of lung edema, and percentage opacification scores all reliably helped predict adverse outcomes in SARS-CoV-2 infection. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Little in this issue.

CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images.

This paper focuses on the application of deep learning (DL) in the diagnosis of coronavirus disease (COVID-19). The novelty of this work is in the introduction of optimized InceptionResNetV2 for COVID-19 (CO-IRv2) method. A part of the CO-IRv2 scheme is derived from the concepts of InceptionNet and ResNet with hyperparameter tuning, while the remaining part is a new architecture consisting of a global average pooling layer, batch normalization, dense layers, and dropout layers. The proposed CO-IRv2 is applied to a new dataset of 2481 computed tomography (CT) images formed by collecting two independent datasets. Data resizing and normalization are performed, and the evaluation is run up to 25 epochs. Various performance metrics, including precision, recall, accuracy, F1-score, area under the receiver operating characteristics (AUC) curve are used as performance metrics. The effectiveness of three optimizers known as Adam, Nadam and RMSProp are evaluated in classifying suspected COVID-19 patients and normal people. Results show that for CO-IRv2 and for CT images, the obtained accuracies of Adam, Nadam and RMSProp optimizers are 94.97%, 96.18% and 96.18%, respectively. Furthermore, it is shown here that for the case of CT images, CO-IRv2 with Nadam optimizer has better performance than existing DL algorithms in the diagnosis of COVID-19 patients. Finally, CO-IRv2 is applied to an X-ray dataset of 1662 images resulting in a classification accuracy of 99.40%.

COVID-19 Diagnosis Using Capsule Network and Fuzzy C-Means and Mayfly Optimization Algorithm.

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C-ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F1-score rate gives the uppermost value compared to the others.

Diagnostic Imaging in Newborns, Children and Adolescents Infected with Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Is There a Realistic Alternative to Lung High-Resolution Computed Tomography (HRCT) and Chest X-Rays? A Systematic Review of the Literature.

Chest computed tomography has been frequently used to evaluate patients with potential coronavirus disease 2019 (COVID-19) infection. However, this may be particularly risky for pediatric patients owing to high doses of ionizing radiation. We sought to evaluate COVID-19 imaging options in pediatric patients based on the published literature. We performed an exhaustive literature review focusing on COVID-19 imaging in pediatric patients. We used the search terms "COVID-19," "SARS-CoV2," "coronavirus," "2019-nCoV," "Wuhan virus," "lung ultrasound (LUS)," "sonography," "lung HRCT," "children," "childhood" and "newborn" to query the online databases PubMed, Medical Subject Headings (MeSH), Embase, LitCovid, the World Health Organization COVID-19 database and Medline Bireme. Articles meeting the inclusion criteria were included in the analysis and review. We identified only seven studies using lung ultrasound (LUS) to diagnose severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in newborns and children. The studies evaluated small numbers of patients, and only 6% had severe or critical illness associated with COVID-19. LUS showed the presence of B-lines in 50% of patients, sub-pleural consolidation in 43.18%, pleural irregularities in 34.09%, coalescent B-lines and white lung in 25%, pleural effusion in 6.82% and thickening of the pleural line in 4.55%. We found 117 studies describing the use of chest X-ray or chest computed tomography in pediatric patients with COVID-19. The proportion of those who were severely or critically ill was similar to that in the LUS study population. Our review indicates that use of LUS should be encouraged in pediatric patients, who are at highest risk of complications from medical ionizing radiation. Increased use of LUS may be of particularly high impact in under-resourced areas, where access to chest computed tomography may be limited.

[Impact of COVID-19 on Nuclear Medicine Procedures in Germany 2020 - Results of a National Survey]. / Auswirkungen von COVID-19 auf nuklearmedizinische Untersuchungen und Therapien im Verlauf von 2020 ­ Ergebnisse einer nationalen Umfrage.

INTRODUCTION: The COVID-19 pandemic imposed an unimaginable challenge to the healthcare systems worldwide. This online survey captured the impact of the COVID-19 pandemic on nuclear medicine services in Germany comparing 2020 to 2019. MATERIALS AND METHODS: A web-based questionnaire was developed to record the 2020 numbers of nuclear medicine procedures and, in particular, the change compared with 2019. The changes in nuclear medicine diagnostics and therapy were queried, as well as the extent to which "Coronavirus SARS-CoV-2" recommendations provided by the DGN were implemented. RESULTS: 91 complete responses were recorded and evaluated. This corresponds to about 20 % of all German nuclear medicine facilities. Nuclear medicine diagnostic tests showed a decrease in scintigraphies for thyroid (15.9 %), bone (8.8 %), lung (7.6 %), sentinel lymph nodes (5.5 %), and myocardium (1.4 %) with small increases in PET/CT examinations (1.2 %) compared with 2019. Among nuclear medicine therapies, reductions were highest for benign indications (benign thyroid 13.3 %, RSO 7.7 %), while changes from 2019 were less pronounced for malignant indications (PRRT: + 2.2 %, PSMA: + 7.4 %, SIRT: -5.9 %, and RJT for thyroid carcinoma -2.4 %). The DGN recommendations for action were fully or partially applied in 90 %. CONCLUSIONS: The initial significant reduction in nuclear medicine procedures in the first three weeks of the COVID-19 pandemic did not continue, but there was no compensation of the previously not performed services. The decrease in diagnostics and therapy procedures of benign diseases was particularly severe.

A young man presenting with encephalopathy and seizures secondary to SARS-CoV-2.

COVID-19 has now emerged from a respiratory illness to a systemic viral illness with multisystem involvement. There is still a lot to learn about this illness as new disease associations with COVID-19 emerge consistently. We present a unique case of a neurological manifestation of a patient with structural brain disease who was COVID-19 positive and developed mental status changes, new-onset seizures and findings suggestive of viral meningitis on lumbar puncture. We also review the literature and discuss our case in the context of the other cases reported. We highlight the value of considering seizures and encephalopathy as one of the presenting features of COVID-19 disease.

Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.

Introduction: The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population. Result: This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values. Conclusion: The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.

The relationship of extent of initial radiological involvement with the need of intensive care, mortality rates, and laboratory parameters in Covid-19

Background/aim: It is very important for the efficient use of limited capacity and the success of treatment to predict patients who may need ICU with high mortality rate in the Covid-19 outbreak. In our study, it was aimed to investigate the value of the radiological involvement on initial CT in demonstrating the ICU transfer and mortality rate of patients. Materials and methods: All PCR-positive patients were included in the study, whose CT, PCR, and laboratory values were obtained simultaneously at the time of first admission. Patients were divided into 4 groups in terms of the extent of radiological lesions. These groups were compared in terms of intensive care transfer needs and Covid-related mortality rates. Results: A total of 477 patients were included in the study. Ninety of them were group 0 (no lung involvement), 162 were group 1 (mild lesion), 89 were group 2 (moderate lesion), and 136 were group 3 (severe lung involvement). A significant relationship was found between the extensiveness of the radiological lesion on CT and admission to intensive care and mortality rate. As the initial radiological involvement amounts increased, the rate of ICU transfer and mortality increased. The mortality rates of the groups were 0%, 3%, 12.3%, and 12.5%, respectively, and the difference was significant (p < 0.001). Similarly, the ICU transfer rates of the groups were 2.2%, 5.6%, 13.5%, and 17.7%, respectively, and the difference was significant (p < 0.001). Conclusion: In conclusion, in our study, the strong relationship between the initial radiological extent assessment and the need for intensive care and mortality rates has been demonstrated, and we believe that our results will make a significant contribution to increase the success of the health system in predicting patients who may progress, helping clinicians and managing pandemics.

Reactive arthritis after COVID-19.

A previously healthy 53-year-old man was hospitalised for 12 days due to COVID-19 with shortness of breath. A few days after discharge from hospital, the patient developed fever and severe pain in several joints in the lower extremities. The pain was so severe that the patient was unable to stand on his feet. Synovial fluid from the right-side knee contained a high number of polynuclear cells and a few mononuclear cells. Microscopy, culture and PCR tests for bacterial infection were all negative. Furthermore, the patient tested negative for rheumatoid factor, anti-cyclic citrullinated peptide and human leukocyte antigen (HLA)-B27. Thus, the condition was compatible with reactive arthritis. The condition improved markedly after a few days' treatment with non-steroid anti-inflammatory drugs and prednisolone.

A new positive SARS-CoV-2 test months after severe COVID-19 illness: reinfection or intermittent viral shedding?

We present a case of a patient who had a history of severe coronavirus disease (COVID-19) 4 months prior to this current presentation and, after a long asymptomatic period, subsequently tested positive for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) by a RNA PCR assay, after several interval negative SARS-CoV-2 RNA tests. We present this potential case of SARS-CoV-2 reinfection in order to incite discussion around differentiating persistent infection with intermittent viral shedding and reinfection, as well as to discuss evolving knowledge and approaches to the clinical management, follow-up molecular testing and treatment of COVID-19 reinfection.

Sentinel Case of COVID-19 at Fort Stewart, GA in a National Guard Soldier Participating in Annual Training: A Case Report.

For healthcare providers, specifically military and federal public health personnel, prompt and accurate diagnosis and isolation of SARS-CoV-2 novel coronavirus patients provide a two-fold benefit: (1) directing appropriate treatment to the infected patient as early as possible in the progression of the disease to increase survival rates and minimize the devastating sequelae following recovery and remission of symptoms; (2) provide critical information requirements that enable commanders and public health officials to best synchronize policy, regulations, and troop movement restrictions while best allocating scarce resources in the delicate balance of risk mitigation versus mission readiness. Simple personal protective measures and robust testing and quarantine procedures, instituted and enforced aggressively by senior leaders, physicians, and healthcare professionals at all levels are an essential aspect of the battle against the COVID-19 pandemic that will determine the success or failure of the overall effort. As consideration, the authors respectfully submit this vignette of the first confirmed positive COVID-19 case presenting to the Emergency Department at Winn Army Community Hospital, Fort Stewart, Georgia.

Nasopharyngeal diffuse large B-cells lymphoma causing acute airway obstruction amid COVID-19 crisis: an anaesthetist's nightmare.

Acute stridor is often an airway emergency. We present a valuable experience handling an elderly woman who was initially treated as COVID-19 positive during the pandemic in November 2020. She needed an urgent tracheostomy due to nasopharyngeal (NP) diffuse large B-cell lymphoma causing acute airway obstruction. Fortunately, 1 hour later, her NP swab real-time PCR test result returned as SARS-CoV-2 negative. This interesting article depicts the importance of adequate preparations when handling potentially infectious patients with anticipated difficult airway and the perioperative issues associated with it.

Utility of Screening Chest Radiographs in Patients with Asymptomatic or Minimally Symptomatic COVID-19 in Singapore.

Background Singapore saw an escalation of coronavirus disease 2019 (COVID-19) cases from fewer than 4000 in April 2020 to more than 40 000 in June 2020, with most of these cases attributed to spread within shared facilities housing foreign workers. Appropriate triage and escalation of clinical care are crucial for this patient group managed in community care facilities (CCFs). Purpose To evaluate the imaging guideline recommendations for COVID-19 from the Fleischner Society and to analyze the clinical utility of screening chest radiography for asymptomatic or minimally symptomatic patients with COVID-19. Materials and Methods In this retrospective study, patients with reverse-transcription polymerase chain reaction-confirmed COVID-19 who were admitted to a designated CCF for continuation of their treatment during May 3-31, 2020, were identified. Upon admission, patients aged 36 years and older without any baseline chest images underwent chest radiography. All chest radiographs and clinical outcomes of patients, including those who were subsequently transferred to acute hospitals for escalation of care, were reviewed. Key proportions of patients with findings of pulmonary infection and those requiring further inpatient treatment were calculated, and 95% binomial proportion CIs were obtained using the Clopper-Pearson method. Results The study included 5621 patients. All patients were men (100%; 5621 of 5621), and the mean patient age was 37 years ± 8 (range, 17-60 years). A total of 1964 chest radiographs were obtained, of which normal images accounted for 98.0% (1925 of 1964 radiographs) and findings of pulmonary infection represented 2.0% (39 of 1964 radiographs). Only 0.2% of patients (four of 1964) with findings of pulmonary infection at chest radiography (all of whom were symptomatic) required supplemental oxygenation and inpatient treatment. None of the asymptomatic patients with findings of pulmonary infection required supplemental oxygenation, and they received only symptomatic treatment. Conclusion In accordance with Fleischner Society recommendations, screening chest radiography is not indicated in patients with coronavirus disease 2019 who are aged 17-60 years with mild or no symptoms unless there is risk of clinical deterioration. © RSNA, 2021 See also the editorial by Schaefer-Prokop and Prokop in this issue.

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks.

BACKGROUND: Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs. METHODS AND FINDINGS: We developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance. CONCLUSIONS: In this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.

Phenotypes and personalized medicine in the acute respiratory distress syndrome.

Although the acute respiratory distress syndrome (ARDS) is well defined by the development of acute hypoxemia, bilateral infiltrates and non-cardiogenic pulmonary edema, ARDS is heterogeneous in terms of clinical risk factors, physiology of lung injury, microbiology, and biology, potentially explaining why pharmacologic therapies have been mostly unsuccessful in treating ARDS. Identifying phenotypes of ARDS and integrating this information into patient selection for clinical trials may increase the chance for efficacy with new treatments. In this review, we focus on classifying ARDS by the associated clinical disorders, physiological data, and radiographic imaging. We consider biologic phenotypes, including plasma protein biomarkers, gene expression, and common causative microbiologic pathogens. We will also discuss the issue of focusing clinical trials on the patient's phase of lung injury, including prevention, administration of therapy during early acute lung injury, and treatment of established ARDS. A more in depth understanding of the interplay of these variables in ARDS should provide more success in designing and conducting clinical trials and achieving the goal of personalized medicine.