Armando Ugo Cavallo*, Jacopo Troisi, Marco Forcina, Pier-Valerio Mari, Valerio Forte, Massimiliano Sperandio, Sergio Pagano, Pierpaolo Cavallo, Roberto Floris and Francesco Garaci
Background: One of the most challenging aspects related to Covid-19 is to establish the presence of infection in early phase of the disease. Texture analysis might be an additional tool for the evaluation of Chest X-ray in patients with clinical suspicion of Covid-19 related pneumonia.
Objective: To evaluate the diagnostic performance of texture analysis and machine learning models for the diagnosis of Covid-19 interstitial pneumonia in Chest X-ray images.
Methods: Chest X-ray 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 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 Chest X-ray images were selected for the final analysis.
Results: 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 Score showing the highest Area Under the Curve (0.971±0.015) was 132.57. Assuming this cut-off the Ensemble 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 Ensemble Machine Learning showed 100% sensitivity, with 80% specificity.
Conclusion: Texture analysis of Chest X-ray 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.
X-ray; COVID-19; Pneumonia; Thorax; Interstitial Pneumonia; Radiomics; Texture Analysis.
Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome,, Department of Medicine, Surgery and Dentistry, “Scuola Medica Salernitana”, University of Salerno,, Division of Radiology, Policlinico Militare Celio, Rome,, Division of Internal Medicine, San Carlo di Nancy Hospital, GVM Care and Research, Rome,, Division of Radiology, San Carlo di Nancy Hospital, GVM Care and Research, Rome,, Division of Radiology, San Carlo di Nancy Hospital, GVM Care and Research, Rome,, Department of Physics “E.R. Caianello”, University of Salerno, Salerno,, Department of Physics “E.R. Caianello”, University of Salerno, Salerno,, Radiology Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome,, Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome