Vascular e-Learning in the MENA Region during the COVID-19 Pandemic

Introduction: With the steady rise in interest in e-learning and the sudden boost provoked by the COVID-19 pandemic, it becomes necessary to explore the e-learning experience within the medical community in the MENA region. Methods: An online survey was conducted during the early phase of the COVID-19 pandemic (June 15 - October 15, 2020). Results: Seventy-eight vascular surgeons and trainees from 16 countries participated. 88% of the participants were male. 55% attended more than 4 activities. More than half of the activities did not lead to any official certification. Topic was the primary determinant for attending an activity. National societies and social media played a major role in disseminating activity-related information. Lack of time, increased workload, differences in time zone, and technical issues were the main obstacles cited. 84.7% of the participants had a positive impression. Conclusion: As the COVID-19 pandemic boosted e-learning activities in vascular surgery, a shift was observed in the learning mode and new leadership skills were called upon. Novel ways of quality control are required.

Performance Analysis of GAN Approaches in the Portable Chest X-Ray Synthetic Image Generation for COVID-19 Screening

COVID-19 mainly affects lung tissues, aspect that makes chest X-ray imaging useful to visualize this damage. In the context of the global pandemic, portable devices are advantageous for the daily practice. Furthermore, Computer-aided Diagnosis systems developed with Deep Learning algorithms can support the clinicians while making decisions. However, data scarcity is an issue that hinders this process. Thus, in this work, we propose the performance analysis of 3 different state-of-the-art Generative Adversarial Networks (GAN) approaches that are used for synthetic image generation to improve the task of automatic COVID-19 screening using chest X-ray images provided by portable devices. Particularly, the results demonstrate a significant improvement in terms of accuracy, that raises 5.28% using the images generated by the best image translation model. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comprehensive Analysis of the Screening of COVID-19 Approaches in Chest X-ray Images from Portable Devices

Computer-aided diagnosis plays an important role in the COVID-19 pandemic. Currently, it is recommended to use X-ray imaging to diagnose and assess the evolution in patients. Particularly, radiologists are asked to use portable acquisition devices to minimize the risk of cross-infection, facilitating an effective separation of suspected patients with other low-risk cases. In this work, we present an automatic COVID-19 screening, considering 6 representative state-of-the-art deep network architectures on a portable chest X-ray dataset that was specifically designed for this proposal. Exhaustive experimentation demonstrates that the models can separate COVID-19 cases from NON-COVID-19 cases, achieving a 97.68% of global accuracy. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

CYCLE GENERATIVE ADVERSARIAL NETWORK APPROACHES TO PRODUCE NOVEL PORTABLE CHEST X-RAYS IMAGES FOR COVID-19 DIAGNOSIS

Coronavirus Disease 2019 (COVID-19), declared a global pandemic by the World Health Organization, mainly affects the pulmonary tissues, playing chest X-ray images an important role for its screening and early detection. In this context, portable X-ray devices are widely used, representing an alternative to fixed devices in order to reduce risks of cross-contamination. However, they provide lower quality and detailed images in terms of spatial resolution and contrast. In this work, given the low availability of images of this recent disease, we present new approaches to artificially increase the dimensionality of portable chest X-ray datasets for COVID-19 diagnosis. Hence, we combined 3 complementary Cycle-GAN architectures to perform a simultaneous oversampling using an unsupervised strategy and without the necessity of paired data. Despite the poor quality of the portable X-ray images, we provide an overall accuracy of 92:50% in a COVID-19 screening context, proving their suitability for COVID-19 diagnostic tasks.

Research exceptionalism and opportunism during the coronavirus pandemic

In the era of COVID19, research has been conducted at an extraordinary pace, eliminating the time from submission to publication to unprecedented levels. This is facilitated by preprint platforms and social media which can spread, reproduce and promote new knowledge with enormous speed. However, there are many concerns regarding the risk of potential deflection from the peer review process that some journals might have adopted, in order to manage the overwhelming wave of COVID19-related submissions. Another dimension of this problem, is the inequity and the publication hurdles that many non-COVID19 scientists might face, since review process of non-COVID19 papers is delayed and journal space is limited to serve the COVID19 literature. Besides the access to publishing, some scientists have redirected their scholarly activity towards coronavirus publications, either permanently or temporarily or even opportunistically. The latter might be attributed to the ease that COVID19 related articles are getting published and cited. This epidemiologic and potentially academic crisis might also be an opportunity for editors, journals and reviewers to create a new journalistic landscape where rapid, transparent and thorough review process can be offered to the authors based on the lessons learned from the current ongoing crisis. © 2020 Zerbinis Publications. All rights reserved.