False negative chest X-Rays in patients affected by COVID-19 pneumonia and corresponding chest CT findings

Radiographic patterns and severity scoring of COVID-19 pneumonia in children: a retrospective study

BACKGROUND

Chest radiography (CXR) is an adjunct tool in treatment planning and monitoring of the disease course of COVID-19 pneumonia. The purpose of the study was to describe the radiographic patterns and severity scores of abnormal CXR findings in children diagnosed with COVID-19 pneumonia.

METHODS

This retrospective study included children with confirmed COVID-19 by reverse transcriptase-polymerase chain reaction test who underwent CXR at the arrival. The CXR findings were reviewed, and modified radiographic scoring was assessed.

RESULTS

The number of abnormal CXR findings was 106 of 976 (10.9%). Ground-glass opacity (GGO) was commonly found in children aged > 9 years (19/26, 73.1%), whereas peribronchial thickening was predominantly found in children aged < 5 years (25/54, 46.3%). Overall, the most common radiographic finding was peribronchial thickening (54/106, 51%). The lower lung zone (56/106, 52.8%) was the most common affected area, and there was neither peripheral nor perihilar predominance (84/106, 79.2%). Regarding the severity of COVID-19 pneumonia based on abnormal CXR findings, 81 of 106 cases (76.4%) had mild lung abnormalities. Moderate and severe lung abnormalities were found in 21 (19.8%) and 4 (3.8%) cases, respectively. While there were no significant differences in the radiographic severity scores among the various pediatric age groups, there were significant disparities in severity scores in the initial CXR and medical treatments.

CONCLUSIONS

This study clarified the age distribution of radiographic features across the pediatric population. GGO was commonly found in children aged > 9 years, whereas peribronchial thickening was predominant in children aged < 5 years. The lower lung zone was the most common affected area, and the high severity lung scores required more medical treatments and oxygen support.

TVFx - CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique

COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets.

The value of case reports in diagnostic radiography

OBJECTIVE

- This paper sought to explore the value of case reports in diagnostic radiography with regards to current usage, relevance to evidence-based radiography, and educational benefits.

KEY FINDINGS

Case reports are short accounts of novel pathologies, trauma or treatment with a critical review of relevant literature. Examples within diagnostic radiography include the appearances of COVID-19 alongside examination-level scenarios involving image artefacts, equipment failure and patient incidents in radiology. With greatest risk of bias and lowest generalisability, they are considered as low-quality evidence with generally poor citation rates. Despite this, there are examples of significant discoveries or developments initiated with case reports with important patient care implications. Furthermore, they offer educational development for both reader and author alike. Whereas the former learns about an unusual clinical scenario, the latter develops scholarly writing skills, reflective practice and may generate further, more complex, research. Radiography-specific case reports could capture the diverse imaging skills and technological expertise currently under-represented in traditional case reports. Potential avenues for cases are broad and may include any imaging modality where patient care or safety of other persons may illicit a teaching point. This encapsulates all stages of the imaging process, before, during and after patient interaction.

CONCLUSION

Despite being low-quality evidence, case reports contribute to evidence-based radiography, add to the knowledge base, and foster a research culture. However, this is contingent upon rigorous peer-review and adherence to ethical treatment of patient data.

IMPLICATIONS FOR PRACTICE

With the drive to increase research engagement and output at all levels in radiography (student to consultant), case reports may act as a realistic grass-root activity for a burdened workforce with limited time and resources.

Chest X-ray in Emergency Radiology: What Artificial Intelligence Applications Are Available?

Due to its widespread availability, low cost, feasibility at the patient's bedside and accessibility even in low-resource settings, chest X-ray is one of the most requested examinations in radiology departments. Whilst it provides essential information on thoracic pathology, it can be difficult to interpret and is prone to diagnostic errors, particularly in the emergency setting. The increasing availability of large chest X-ray datasets has allowed the development of reliable Artificial Intelligence (AI) tools to help radiologists in everyday clinical practice. AI integration into the diagnostic workflow would benefit patients, radiologists, and healthcare systems in terms of improved and standardized reporting accuracy, quicker diagnosis, more efficient management, and appropriateness of the therapy. This review article aims to provide an overview of the applications of AI for chest X-rays in the emergency setting, emphasizing the detection and evaluation of pneumothorax, pneumonia, heart failure, and pleural effusion.

New patch-based strategy for COVID-19 automatic identification using chest x-ray images

Purpose

The development of a robust model for automatic identification of COVID-19 based on chest x-rays has been a widely addressed topic over the last couple of years; however, the scarcity of good quality images sets, and their limited size, have proven to be an important obstacle to obtain reliable models. In fact, models proposed so far have suffered from over-fitting erroneous features instead of learning lung features, a phenomenon known as shortcut learning. In this research, a new image classification methodology is proposed that attempts to mitigate this problem.

Methods

To this end, annotation by expert radiologists of a set of images was performed. The lung region was then segmented and a new classification strategy based on a patch partitioning that improves the resolution of the convolution neural network is proposed. In addition, a set of native images, used as an external evaluation set, is released.

Results

The best results were obtained for the 6-patch splitting variant with 0.887 accuracy, 0.85 recall and 0.848 F1score on the external validation set.

Conclusion

The results show that the proposed new strategy maintains similar values between internal and external validation, which gives our model generalization power, making it available for use in hospital settings.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12553-022-00704-4.

COVID-19 Detection from Chest X-rays Using Trained Output Based Transfer Learning Approach

The recent Coronavirus disease (COVID-19), which started in 2019, has spread across the globe and become a global pandemic. The efficient and effective COVID-19 detection using chest X-rays helps in early detection and curtailing the spread of the disease. In this paper, we propose a novel Trained Output-based Transfer Learning (TOTL) approach for COVID-19 detection from chest X-rays. We start by preprocessing the Chest X-rays of the patients with techniques like denoising, contrasting, segmentation. These processed images are then fed to several pre-trained transfer learning models like InceptionV3, InceptionResNetV2, Xception, MobileNet, ResNet50, ResNet50V2, VGG16, and VGG19. We fine-tune these models on the processed chest X-rays. Then we further train the outputs of these models using a deep neural network architecture to achieve enhanced performance and aggregate the capabilities of each of them. The proposed model has been tested on four recent COVID-19 chest X-rays datasets by computing several popular evaluation metrics. The performance of our model has also been compared with various deep transfer learning models and several contemporary COVID-19 detection methods. The obtained results demonstrate the efficiency and efficacy of our proposed model.

The Role of Ionizing Radiation for Diagnosis and Treatment against COVID-19: Evidence and Considerations

The Coronavirus disease 2019 (COVID-19) pandemic continues to spread worldwide with over 260 million people infected and more than 5 million deaths, numbers that are escalating on a daily basis. Frontline health workers and scientists diligently fight to alleviate life-threatening symptoms and control the spread of the disease. There is an urgent need for better triage of patients, especially in third world countries, in order to decrease the pressure induced on healthcare facilities. In the struggle to treat life-threatening COVID-19 pneumonia, scientists have debated the clinical use of ionizing radiation (IR). The historical literature dating back to the 1940s contains many reports of successful treatment of pneumonia with IR. In this work, we critically review the literature for the use of IR for both diagnostic and treatment purposes. We identify details including the computed tomography (CT) scanning considerations, the radiobiological basis of IR anti-inflammatory effects, the supportive evidence for low dose radiation therapy (LDRT), and the risks of radiation-induced cancer and cardiac disease associated with LDRT. In this paper, we address concerns regarding the effective management of COVID-19 patients and potential avenues that could provide empirical evidence for the fight against the disease.

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.

The Role and Challenges of Clinical Imaging During COVID-19 Outbreak

Objective:

The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them.

Materials and Methods:

A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles.

Results:

A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students.

Conclusion:

The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.

Current limitations to identify covid-19 using artificial intelligence with chest x-ray imaging (part ii). The shortcut learning problem

Since the outbreak of the COVID-19 pandemic, computer vision researchers have been working on automatic identification of this disease using radiological images. The results achieved by automatic classification methods far exceed those of human specialists, with sensitivity as high as 100% being reported. However, prestigious radiology societies have stated that the use of this type of imaging alone is not recommended as a diagnostic method. According to some experts the patterns presented in these images are unspecific and subtle, overlapping with other viral pneumonias. This report seeks to evaluate the analysis the robustness and generalizability of different approaches using artificial intelligence, deep learning and computer vision to identify COVID-19 using chest X-rays images. We also seek to alert researchers and reviewers to the issue of "shortcut learning". Recommendations are presented to identify whether COVID-19 automatic classification models are being affected by shortcut learning. Firstly, papers using explainable artificial intelligence methods are reviewed. The results of applying external validation sets are evaluated to determine the generalizability of these methods. Finally, studies that apply traditional computer vision methods to perform the same task are considered. It is evident that using the whole chest X-Ray image or the bounding box of the lungs, the image regions that contribute most to the classification appear outside of the lung region, something that is not likely possible. In addition, although the investigations that evaluated their models on data sets external to the training set, the effectiveness of these models decreased significantly, it may provide a more realistic representation as how the model will perform in the clinic. The results indicate that, so far, the existing models often involve shortcut learning, which makes their use less appropriate in the clinical setting.

COVID-19 Pneumonia: The Great Ultrasonography Mimicker

The pandemic spread of the new severe acute respiratory syndrome coronavirus 2 has raised the necessity to identify an appropriate imaging method for early diagnosis of coronavirus disease 2019 (COVID-19). Chest computed tomography (CT) has been regarded as the mainstay of imaging evaluation for pulmonary involvement in the early phase of the pandemic. However, due to the poor specificity of the radiological pattern and the disruption of radiology centers' functionality linked to an excessive demand for exams, the American College of Radiology has advised against CT use for screening purposes. Lung ultrasound (LUS) is a point-of-care imaging tool that is quickly available and easy to disinfect. These advantages have determined a "pandemic" increase of its use for early detection of COVID-19 pneumonia in emergency departments. However, LUS findings in COVID-19 patients are even less specific than those detectable on CT scans. The scope of this perspective article is to discuss the great number of diseases and pathologic conditions that may mimic COVID-19 pneumonia on LUS examination.

Lung Ultrasound in the Diagnosis of COVID-19 Pneumonia: Not Always and Not Only What Is COVID-19 "Glitters"

Background: In the current coronavirus disease-2019 (COVID-19) pandemic, lung ultrasound (LUS) has been extensively employed to evaluate lung involvement and proposed as a useful screening tool for early diagnosis in the emergency department (ED), prehospitalization triage, and treatment monitoring of COVID-19 pneumonia. However, the actual effectiveness of LUS in characterizing lung involvement in COVID-19 is still unclear. Our aim was to evaluate LUS diagnostic performance in assessing or ruling out COVID-19 pneumonia when compared with chest CT (gold standard) in a population of SARS-CoV-2-infected patients. Methods: A total of 260 consecutive RT-PCR confirmed SARS-CoV-2-infected patients were included in the study. All the patients underwent both chest CT scan and concurrent LUS at admission, within the first 6-12 h of hospital stay. Results: Chest CT scan was considered positive when showing a "typical" or "indeterminate" pattern for COVID-19, according to the RSNA classification system. Disease prevalence for COVID-19 pneumonia was 90.77%. LUS demonstrated a sensitivity of 56.78% in detecting lung alteration. The concordance rate for the assessment of abnormalities by both methods increased in the case of peripheral distribution and middle-lower lung location of lesions and in cases of more severe lung involvement. A total of nine patients had a "false-positive" LUS examination. Alternative diagnosis included chronic heart disease (six cases), bronchiectasis (two cases), and subpleural emphysema (one case). LUS specificity was 62.50%. Collateral findings indicative of overlapping conditions at chest CT were recorded also in patients with COVID-19 pneumonia and appeared distributed with increasing frequency passing from the group with mild disease (17 cases) to that with severe disease (40 cases). Conclusions: LUS does not seem to be an adequate tool for screening purposes in the ED, due to the risk of missing some lesions and/or to underestimate the actual extent of the disease. Furthermore, the not specificity of LUS implies the possibility to erroneously classify pre-existing or overlapping conditions as COVID-19 pneumonia. It seems more safe to integrate a positive LUS examination with clinical, epidemiological, laboratory, and radiologic findings to suggest a "virosis." Viral testing confirmation is always required.

Challenges and optimization strategies in medical imaging service delivery during COVID-19

In coronavirus disease 2019 (COVID-19), medical imaging plays an essential role in the diagnosis, management and disease progression surveillance. Chest radiography and computed tomography are commonly used imaging techniques globally during this pandemic. As the pandemic continues to unfold, many healthcare systems worldwide struggle to balance the heavy strain due to overwhelming demand for healthcare resources. Changes are required across the entire healthcare system and medical imaging departments are no exception. The COVID-19 pandemic had a devastating impact on medical imaging practices. It is now time to pay further attention to the profound challenges of COVID-19 on medical imaging services and develop effective strategies to get ahead of the crisis. Additionally, preparation for operations and survival in the post-pandemic future are necessary considerations. This review aims to comprehensively examine the challenges and optimization of delivering medical imaging services in relation to the current COVID-19 global pandemic, including the role of medical imaging during these challenging times and potential future directions post-COVID-19.

Digital imaging, technologies and artificial intelligence applications during COVID-19 pandemic

The advancement of technology remained an immersive interest for humankind throughout the past decades. Tech enterprises offered a stream of innovation to address the universal healthcare concerns. The novel coronavirus holds a substantial foothold of planet earth which is combatted by digital interventions across afflicted geographical boundaries and territories. This study aims to explore the trends of modern healthcare technologies and Artificial Intelligence (AI) during COVID-19 crisis, define the concepts and clinical role of AI in the mitigation of COVID-19, investigate and correlate the efficacy of AI-enabled technology in medical imaging during COVID-19 and determine advantages, drawbacks, and challenges of artificial intelligence during COVID-19 pandemic. The paper applied systematic review approach using a deliberated research protocol and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart. Digital technologies can coordinate COVID-19 responses in a cascade fashion that extends from the clinical care facility to the exterior of the pending viral epicenter. With cases of healthcare robotics, aerial drones, and the internet of things as evidentiary examples. PCR tests and medical imaging are the frontier diagnostics of COVID-19. Computed tomography helped to correct the accuracy variation of PCR tests at a clinical sensitivity of 98 %. Artificial intelligence can enable autonomous COVID-19 responses using techniques like machine learning. Technology could be an endless system of innovation and opportunities when sourced effectively. Scientists can utilize technology to resolve global concerns challenging the history of tangible possibility. Digital interventions have enhanced the responses to COVID-19, magnified the role of medical imaging amid the COVID-19 crisis and have exposed healthcare professionals to the opportunity of contactless care.

Low Sensitivity of Admission Lung US Compared to Chest CT for Diagnosis of Lung Involvement in a Cohort of 82 Patients with COVID-19 Pneumonia

Background and Objectives: The potential role of lung ultrasound (LUS) in characterizing lung involvement in Coronavirus disease 2019 (COVID-19) is still debated. The aim of the study was to estimate sensitivity of admission LUS for the detection of SARS-CoV-2 lung involvement using Chest-CT (Computed Tomography) as reference standard in order to assess LUS usefulness in ruling out COVID-19 pneumonia in the Emergency Department (ED). Methods: Eighty-two patients with confirmed COVID-19 and signs of lung involvement on Chest-CT were consecutively admitted to our hospital and recruited in the study. Chest-CT and LUS examination were concurrently performed within the first 6-12h from admission. Sensitivity of LUS was calculated using CT findings as a reference standard. Results: Global LUS sensitivity in detecting COVID-19 pulmonary lesions was 52%. LUS sensitivity ranged from 8% in case of focal and sporadic ground-glass opacities (mild disease), to 52% for a crazy-paving pattern (moderate disease) and up to 100% in case of extensive subpleural consolidations (severe disease), although LUS was not always able to detect all the consolidations assessed at Chest-CT. LUS sensitivity was higher in detecting a typical Chest-CT pattern (60%) and abnormalities showing a middle-lower zone predominance (79%). Conclusions: As admission LUS may result falsely negative in most cases, it should not be considered as a reliable imaging tool in ruling out COVID-19 pneumonia in patients presenting in ED. It may at least represent an expanded clinical evaluation that needs integration with other diagnostic tests (e.g., nasopharyngeal swab, Chest-CT).

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.

Towards a sensitive and accurate interpretation of molecular testing for SARS-CoV-2: a rapid review of 264 studies

BackgroundSensitive molecular diagnostics and correct test interpretation are crucial for accurate COVID-19 diagnosis and thereby essential for good clinical practice. Furthermore, they are a key factor in outbreak control where active case finding in combination with isolation and contact tracing are crucial.AimWith the objective to inform the public health and laboratory responses to the pandemic, we reviewed current published knowledge on the kinetics of SARS-CoV-2 infection as assessed by RNA molecular detection in a wide range of clinical samples.MethodsWe performed an extensive search on studies published between 1 December 2019 and 15 May 2020, reporting on molecular detection and/or isolation of SARS-CoV-2 in any human laboratory specimen.ResultsWe compiled a dataset of 264 studies including 32,515 COVID-19 cases, and additionally aggregated data points (n = 2,777) from sampling of 217 adults with known infection timeline. We summarised data on SARS-CoV-2 detection in the respiratory and gastrointestinal tract, blood, oral fluid, tears, cerebrospinal fluid, peritoneal fluid, semen, vaginal fluid; where provided, we also summarised specific observations on SARS-CoV-2 detection in pregnancy, infancy, children, adolescents and immunocompromised individuals.ConclusionOptimal SARS-CoV-2 molecular testing relies on choosing the most appropriate sample type, collected with adequate sampling technique, and with the infection timeline in mind. We outlined knowledge gaps and directions for future well-documented systematic studies.

Modifications to mobile chest radiography technique during the COVID-19 pandemic - implications of X-raying through side room windows

INTRODUCTION

Modifications to common radiographic techniques have resulted from the challenges presented by the COVID-19 pandemic. Reports exist regarding the potential benefits of undertaking mobile radiography through side room windows. The aim of this study was to evaluate the impact on image quality and exposure factors when undertaking such examinations.

METHODS

A phantom based study was undertaken using a digital X-ray room. Control acquisitions, using a commercially available image quality test tool, were performed using standard mobile chest radiography acquisition factors. Image quality (physical and visual), incidence surface air kerma (ISAK), Exposure Index (EI) and Deviation Index (DI) were recorded. Image quality and radiation dose were further assessed for two additional (experimental) scenarios, where a side room window was located immediately adjacent to the exit port of the light beam diaphragm. The goal of experimental scenario one was to modify exposure factors to maintain the control ISAK. The goal of experimental scenario two was to modify exposure factors to maintain the control EI and DI. Dose and image quality data were compared between the three scenarios.

RESULTS

To maintain the pre-window (control) ISAK (76 μGy), tube output needed a three-fold increase (90 kV/4 mAs versus 90 kV/11.25 mAs). To maintain EI/DI a more modest increase in tube output was required (90 kV/8 mAs/ISAK 54 μGy). Physical and visual assessments of spatial resolution and signal-to-noise ratio were indifferent between the three scenarios. There was a slight statistically significant reduction in contrast-to-noise ratio when imaging through the glass window (2.3 versus 1.4 and 1.2; P = 0.005).

CONCLUSION

Undertaking mobile X-ray examinations through side room windows is potentially feasible but does require an increase in tube output and is likely to be limited by minor reductions in image quality.

IMPLICATIONS FOR PRACTICE

Mobile examinations performed through side room windows should only be used in limited circumstances and future clinical evaluation of this technique is warranted.

The challenge of deciding between home-discharge versus hospitalization in COVID-19 patients: The role of initial imaging and clinicolaboratory data

Background/Objective

It is important to predict the COVID-19 patient's prognosis, particularly in countries with lack or deficiency of medical resource for patient's triage management. Currently, WHO guideline suggests using chest imaging in addition to clinicolaboratory evaluation to decide on triage between home-discharge versus hospitalization. We designed our study to validate this recommendation to guide clinicians. This study providing some suggestions to guide clinicians for better decision making in 2020.

Methods

In this retrospective study, patients with RT-PCR confirmed COVID-19 (N = 213) were divided in different clinical and management scenarios: home-discharge, ward hospitalization and ICU admission. We reviewed the patient's initial chest CT if available. We evaluated quantitative and qualitative characteristics of CT as well as relevant available clinicolaboratory data. Chi-square, One-Way ANOVA and Paired t-test were used for analysis.

Results

The finding showed that most patients with mixed patterns, pleural effusion, 5 lobes involved, total score ≥10, SpO2% ≤ 90, ESR (mm/h) ≥ 60 and WBC (10³/μL) ≥ 8000 were hospitalized. Most patients with Ground-glass opacities only, ≤3 lobes involvement, peripheral distribution, SpO2% ≥ 95, ESR (mm/h) < 30 and WBC(10³/μL) < 6000 were home-discharged.

Conclusions

This study suggests the use of initial chest CT (qualitative and quantitative evaluation) in addition to initial clinicolaboratory data could be a useful supplementary method for clinical management and it is an excellent decision making tool (home-discharge versus ICU/Ward admission) for clinicians.

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy.

Évaluation d'un dispositif de continuité pédagogique à distance mis en place auprès d'étudiants MERM pendant le confinement sanitaire lié au COVID-19

Introduction

The specific context related to the COVID-19 pandemic necessitated the implementation of distance learning continuity for students. In France, teachers and radiography students in initial training, not specially prepared for this, had to adapt. An evaluation of the system was proposed to the students.

Materials and methods

An anonymous online questionnaire with 4 main sections (pedagogy, communication, learning and concerns) was sent to 91 students at the end of the semester.

Results

91 responses were received. The slideshows with sound or presented during a virtual class are appreciated by the students. Online quizzes are ideal for learning/reviewing. For assessments, individual assignments and online questionnaires are appreciated. Teacher/student interaction via e-mail or video conferencing was considered satisfactory by the large majority of students. Student-student interactions via social networks, for course explanations or document exchange, are very suitable. The majority of students felt they were working a lot and much more compared to face-to-face teaching. Less than half of the students worked more than 20 h per week. Their motivation varied widely. Organizational habits were disrupted, but the autonomy granted was appreciated. The students were mainly concerned about the health of their loved ones and not about their own health.

Discussion

The use of distance education tools requires teacher commitment and technical skills. The frequency of communication by e-mail and/or videoconference between members of the teaching team and students must be adapted to the situation. Exchanges by e-mail allow for traceability, while videoconferencing allows direct interaction and a way out of isolation. Autonomy, appreciated by the students, was nevertheless combined with a strong variation in motivation; the anxiety-provoking period in which pedagogical continuity was built up may explain this contradictory observation.

Conclusion

The results obtained largely confirm the data in the literature. The experience gained through this survey should lead teachers to continue their reflection by test/integrating and evaluating distance education systems, while continuing face-to-face activities.

COVID-19: Progress in diagnostics, therapy and vaccination

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has recently become a pandemic. As the sudden emergence and rapid spread of SARS-CoV-2 is endangering global health and the economy, the development of strategies to contain the virus's spread are urgently needed. At present, various diagnostic kits to test for SARS-CoV-2 are available for use to initiate appropriate treatment faster and to limit further spread of the virus. Several drugs have demonstrated in vitro activity against SARS-CoV-2 or potential clinical benefits. In addition, institutions and companies worldwide are working tirelessly to develop treatments and vaccines against COVID-19. However, no drug or vaccine has yet been specifically approved for COVID-19. Given the urgency of the outbreak, we focus here on recent advances in the diagnostics, treatment, and vaccine development for SARS-CoV-2 infection, helping to guide strategies to address the current COVID-19 pandemic.