COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis.

Purpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT). Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (

Computed tomography findings in a Brazilian cohort of 48 patients with pneumonia due to coronavirus disease.

INTRODUCTION: This study was conducted to retrospectively review chest computed tomography (CT) findings in a Brazilian cohort of patients with pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: Chest CT scans of 78 patients with confirmed coronavirus disease (COVID-19), obtained in March and April 2020, were reviewed. Of 78 cases, the CT scans of 48 (61.5%) showed lung opacities. CT opacity features, their distribution, and the extent of infiltration were evaluated. RESULTS: The most common CT findings were ground-glass opacities (97.9%), crazy-paving pattern (58.3%), and mixed pattern (18.8%). Rounded lung opacities were observed most frequently (70.8%). Other findings were cystic airspace changes (37.5%), vascular dilatation (35.4%), and the organizing pneumonia pattern (14.6%). The findings were frequently bilateral (87.5%), symmetrical (68.9%), and peripheral (60.2%). CONCLUSIONS: The most common CT findings were ground-glass opacities and the crazy-paving pattern. Involvement was mostly bilateral, symmetrical, and peripheral. Round opacity morphology was frequently observed and might have some degree of specificity to viral COVID-19 pneumonia.

Computed tomography findings in a Brazilian cohort of 48 patients with pneumonia due to coronavirus disease

Abstract INTRODUCTION: This study was conducted to retrospectively review chest computed tomography (CT) findings in a Brazilian cohort of patients with pneumonia caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: Chest CT scans of 78 patients with confirmed coronavirus disease (COVID-19), obtained in March and April 2020, were reviewed. Of 78 cases, the CT scans of 48 (61.5%) showed lung opacities. CT opacity features, their distribution, and the extent of infiltration were evaluated. RESULTS: The most common CT findings were ground-glass opacities (97.9%), crazy-paving pattern (58.3%), and mixed pattern (18.8%). Rounded lung opacities were observed most frequently (70.8%). Other findings were cystic airspace changes (37.5%), vascular dilatation (35.4%), and the organizing pneumonia pattern (14.6%). The findings were frequently bilateral (87.5%), symmetrical (68.9%), and peripheral (60.2%). CONCLUSIONS: The most common CT findings were ground-glass opacities and the crazy-paving pattern. Involvement was mostly bilateral, symmetrical, and peripheral. Round opacity morphology was frequently observed and might have some degree of specificity to viral COVID-19 pneumonia.