Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy*

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). Texturebased 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 threedimensional 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.

Automated summary

This study aimed to quantify lung modifications in COVID-19 and IPF patients using a three-dimensional multiscale fuzzy entropy algorithm, statistically showing significant differences in 9 scale factors, with high classification accuracy and sensitivity.