The Role of 3D CT Imaging in the Accurate Diagnosis of Lung Function in Coronavirus Patients.

Early grading of coronavirus disease 2019 (COVID-19), as well as ventilator support machines, are prime ways to help the world fight this virus and reduce the mortality rate. To reduce the burden on physicians, we developed an automatic Computer-Aided Diagnostic (CAD) system to grade COVID-19 from Computed Tomography (CT) images. This system segments the lung region from chest CT scans using an unsupervised approach based on an appearance model, followed by 3D rotation invariant Markov-Gibbs Random Field (MGRF)-based morphological constraints. This system analyzes the segmented lung and generates precise, analytical imaging markers by estimating the MGRF-based analytical potentials. Three Gibbs energy markers were extracted from each CT scan by tuning the MGRF parameters on each lesion separately. The latter were healthy/mild, moderate, and severe lesions. To represent these markers more reliably, a Cumulative Distribution Function (CDF) was generated, then statistical markers were extracted from it, namely, 10th through 90th CDF percentiles with 10% increments. Subsequently, the three extracted markers were combined together and fed into a backpropagation neural network to make the diagnosis. The developed system was assessed on 76 COVID-19-infected patients using two metrics, namely, accuracy and Kappa. In this paper, the proposed system was trained and tested by three approaches. In the first approach, the MGRF model was trained and tested on the lungs. This approach achieved 95.83% accuracy and 93.39% kappa. In the second approach, we trained the MGRF model on the lesions and tested it on the lungs. This approach achieved 91.67% accuracy and 86.67% kappa. Finally, we trained and tested the MGRF model on lesions. It achieved 100% accuracy and 100% kappa. The results reported in this paper show the ability of the developed system to accurately grade COVID-19 lesions compared to other machine learning classifiers, such as k-Nearest Neighbor (KNN), decision tree, naïve Bayes, and random forest.

COVID-19 and myocarditis: a brief review.

Cardiovascular complications (especially myocarditis) related to COVID-19 viral infection are not well understood, nor do they possess a well recognized diagnostic protocol as most of our information regarding this issue was derived from case reports. In this article we extract data from all published case reports in the second half of 2020 to summarize the theories of pathogenesis and explore the value of each diagnostic test including clinical, lab, ECG, ECHO, cardiac MRI and endomyocardial biopsy. These tests provide information that explain the mechanism of development of myocarditis that further paves the way for better management.

Abdominal Computed Tomography Angiography and Venography in Evaluation of Hemorrhagic and Thrombotic lesions in Hospitalized COVID-19 patients.

PURPOSE: To report imaging findings at computed tomography angiography (CTA) and venography (CTV) of the abdomen and pelvis in evaluation of hemorrhagic and thrombotic lesions in hospitalized patients with COVID-19. METHODS: In this retrospective observational study, patients admitted to a single tertiary care center from April 1 to July 20, 2020, who tested positive for SARS-CoV-2 and developed acute abdominal pain or decreasing hemoglobin levels over the course of hospitalization were included. Abdominal CTA/CTV imaging studies performed in these patients were reviewed, and acute hemorrhagic or thromboembolic findings were recorded. RESULTS: A total of 40 patients (mean age, 59.7 years; 20 men, 20 women) were evaluated. Twenty-five patients (62.5%) required intensive care unit (ICU) admission and 15 patients (37.5%) were treated in the medical ward. Hemorrhagic complications were detected in 19 patients (47.5%), the most common was intramuscular hematoma diagnosed in 17 patients; It involved the iliopsoas compartment unilaterally in 10 patients, bilaterally in 2 patients and the rectus sheath in 5 cases. Pelvic extraperitoneal hemorrhage was found in 3 patients, and mesenteric hematoma in one patient. Thromboembolic events were diagnosed in 8 patients (20%) including; arterial thrombosis (n = 2), venous thrombosis (n = 2), splenic infarct (n = 1), bowel ischemia (n = 1) and multiple sites of thromboembolism (n = 2). CONCLUSION: Our study highlights that both hemorrhagic and thromboembolic complications can be seen in hospitalized patients with COVID-19. It is important that radiologists maintain a high index of suspicion for early diagnosis of these complications.