Pulmonary analysis using 3D multiscale entropy of healthy, IPF, and COVID-19 cases.
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The 3 groups are statistically different for 9 scale factors ()
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Complexity index (CI) based on the sum of entropy values for group classification.
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Threshold and machine-learning classification models for COVID-19 and healthy cases.
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 (). 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 , a sensitivity of , and a specificity of . Moreover, 4 different machine-learning models were also used to classify the same COVID-19 dataset for comparison purposes.
This work was supported by FCT – Fundação para a Ciência e Tecnologia under the project UID/04559/2020 to fund the activities of LIBPhys-UC – Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics and the project PTDC/EMD-TLM/30295/2017 European Regional – Development Fund (PT-COMPETE 2020).