ABSTRACT
Coronavirus disease 2019 (COVID-19) is a pandemic that spread rapidly around the world, causing an enormous overload on the health systems of the different affected countries. Among the many different manifestations of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, an uncommon complication is the development of pneumomediastinum. In the clinical case presented, the patient was diagnosed with COVID-19 pneumonia and due to severe refractory hypoxemia, she was submitted to therapy with non-invasive ventilation (NIV). After initial stabilization and improvement, there was unexpected clinical deterioration and pneumomediastinum was diagnosed. The purpose of this report is to highlight the importance of considering pneumomediastinum as a complication of COVID-19 pneumonia in cases subjected to non-invasive ventilation.
ABSTRACT
Radiologists, and doctors in general, need relevant information for the quantification and characterization of pulmonary structures damaged by severe diseases, such as the Coronavirus disease 2019 (COVID-19). Texture-based analysis in scope of other pulmonary diseases has been used to screen, monitor, and provide valuable information for several kinds of diagnoses. To differentiate COVID-19 patients from healthy subjects and patients with other pulmonary diseases is crucial. Our goal is to quantify lung modifications in two pulmonary pathologies: COVID-19 and idiopathic pulmonary fibrosis (IPF). For this purpose, we propose the use of a three-dimensional multiscale fuzzy entropy (MFE3D) algorithm. The three groups tested (COVID-19 patients, IPF, and healthy subjects) were found to be statistically different for 9 scale factors ( p < 0.01 ). A complexity index (CI) based on the sum of entropy values is used to classify healthy subjects and COVID-19 patients showing an accuracy of 89.6 % , a sensitivity of 96.1 % , and a specificity of 76.9 % . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.