Recurrent floating common carotid artery thrombus related to COVID-19: A case report.

The occurrence of arterial and venous thrombosis during coronavirus infection has been widely reported since the beginning of the epidemic. Floating carotid thrombus (FCT) in the common carotid artery is exceptional and its main known cause is atherosclerosis. We describe the case of a 54-year-old man who developed, one week after the onset symptomatology of related to COVID-19 infection, an ischemic stroke, complicating a large intraluminal floating thrombus in the left common carotid artery. Despite surgery and anticoagulation, a local recurrence with other thrombotic complications occurred and the patient died.

Recurrent floating common carotid artery thrombus related to COVID-19: a case report

The occurrence of arterial and venous thrombosis during coronavirus infection has been widely reported since the beginning of the epidemic. Floating carotid thrombus (FCT) in the common carotid artery is exceptional, and its main known cause is atherosclerosis. We describe the case of a 54-year-old man who developed, one week after the onset symptomatology of related to COVID-19 infection, an ischemic stroke, complicating a large intraluminal floating thrombus in the left common carotid artery. Despite surgery and anticoagulation, a local recurrence with other thrombotic complications occurred and the patient died.

Asymptomatic covid-19 patients: Experience and lessons from a developing country

The world is facing a pandemic due to the SARS-Coronavirus 2, since late 2019. Many questions remain unanswered regarding the disease course. Key amongst its talking point is the case of asymptomatic patients. This potentially jeopardizes infection control strategy as asymptomatic cases are difficult to identify and hence difficult to isolate. Our study intends to define the clinical and radiological features of asymptomatic COVID-19 cases, the disease course as well as highlight the role of chest CT in its management. This is a monocentric study involving 114 asymptomatic adults admitted in our COVID-19 Unit. Clinical, radiological and laboratory findings were retrospectively analyzed. Chi-squared, Fisher exact test and the student test were used for statistical analysis. Asymptomatic patients represent 51.81% of patients. There was a slight male predominance with a mean age of 37.64 years. Patients with abnormal CT had a longer hospital stay than those with unremarkable CT and even more so were older. None of the patients presented severe or critical extension of parenchymal lesions. Only two patients (4.54%) with normal CT on admission presented with abnormalities on control CT. Cases with worsening CT were older with bilateral pulmonary involvement. All patients remained asymptomatic on treatment. Even when asymptomatic, COVID-19 patients present mild lung lesions. The positivity of the initial chest CT imaging is directly correlated to the disease course. Older patients with bilateral pulmonary lesions are more likely to worsen and should be closely monitored. Moreover, it is safe to manage asymptomatic patients with normal CT in a non-hospital setting.

Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images

To control the spread of the COVID-19 virus and to gain critical time in controlling the spread of the disease, rapid and accurate diagnostic methods based on artificial intelligence are urgently needed. In this article, we propose a clinical decision support system for the early detection of COVID 19 using deep learning based on chest radiographic images. For this we will develop an in-depth learning method which could extract the graphical characteristics of COVID-19 in order to provide a clinical diagnosis before the test of the pathogen. For this, we collected 100 images of cases of COVID-19 confirmed by pathogens, 100 images diagnosed with typical viral pneumonia and 100 images of normal cases. The architecture of the proposed model first goes through a preprocessing of the input images followed by an increase in data. Then the model begins a step to extract the characteristics followed by the learning step. Finally, the model begins a classification and prediction process with a fully connected network formed of several classifiers. Deep learning and classification were carried out using the VGG convolutional neural network. The proposed model achieved an accuracy of 92.5% in internal validation and 87.5% in external validation. For the AUC criterion we obtained a value of 97% in internal validation and 95% in external validation. Regarding the sensitivity criterion, we obtained a value of 92% in internal validation and 87% in external validation. The results obtained by our model in the test phase show that our model is very effective in detecting COVID-19 and can be offered to health communities as a precise, rapid and effective clinical decision support system in COVID-19 detection. © 2020 IEEE.

Early detection of COVID19 by deep learning transfer Model for populations in isolated rural areas

To combat the spread of COVID 19, the World Health Organization suggests a large-scale implementation of COVID 19 tests. Unfortunately, these tests are expensive and cannot be provided and available for people in rural and remote areas. To remedy this problem, we will develop an intelligent clinical decision support system (SADC) for the early diagnosis of COVID 19 from chest X-rays which are more accessible for people in rural areas. Thus, we collected a total of 566 radiological images classified into 3 classes: a class of COVID19 type, a Class of Pneumonia type and a class of Normal type. In the experimental analysis, 70% of the data set was used as training set and 30% was used as the test set. After preprocessing process, we use some augmentation using a rotation, a horizontal flip, a channel shift and rescale. Our finale classifier achieved the best performance with test accuracy of 99%, f1score 98%, precision of 98.60% and sensitivity 98.30%. © 2020 IEEE.