Texture Analysis in the Evaluation of Covid-19 Pneumonia in Chest X-Ray Images: a Proof of Concept Study

Abstract Objectives: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Recently Artificial Intelligence systems for the diagnosis of Covid-19 related pneumonia on Chest X ray (CXR) or chest CT have been tested with variable, but not negligible, accuracy. Texture analysis might be an additional tool for the evaluation of CXR in patients with clinical suspicion of Covid-19 related pneumonia.Methods: CXR images were accessed from a publicly available repository (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database). Lung areas were manually segmented using a polygonal regions of interest (ROI) covering both lung areas, using MaZda, a freely available software for texture analysis. A total of 308 features per ROI was extracted. One hundred-ten Covid-19 CXR images were selected for the final analysisResults: Six models, namely NB, GLM, DL, GBT, ANN and PLS-DA were selected and ensembled. According to Youden’s index, the Covid-19 Ensemble Machine Learning (EML)-Score showing the highest AUCROC (0.971±0.015) was 132.57. Assuming this cut-off the EML model performance was estimated evaluating both true and false positive/negative, resulting in 91.8% accuracy with 93% sensitivity and 90% specificity. Moving the cut-off value to -100, although the accuracy resulted lower (90.6%), the EML showed 100% sensitivity, with 80% specificityConclusion: Texture analysis of CXR images and machine learning algorithms may help in differentiating patients with Covid-19 pneumonia. Despite several limitations, this study can lay ground for future researches in this field and help developing more rapid and accurate screening tools for these patients.