Radiomics is a rapidly evolving field which assumes that textural features of medical images may reflect underlying microstructural features associated to specific disease. Artificial intelligence (AI) may improve the quantification and the diagnostic accuracy of radiomic features. Thus, analysis of radiomic features with AI systems has the potential to improve patient stratification, treatment planning, and therapy monitoring, and may be combined with clinical and genomic data.
In this scenario, well balanced and high accuracy texture analysis algorithms may speed up the clinical work up of patients with suspected SARS-CoV–2 infection.
To date, AI diagnostic systems for Covid–19 interstitial lung disease have been implemented in CT and chest X-ray images with variable but not negligible accuracy [18–20]. These approaches show a good diagnostic accuracy but do not consider textural features of images. A combined approach of AI and radiomic feature extraction and analysis may help in rapidly identifying Covid–19 patients and, additionally, might help in understanding textural and histological features of Covid–19 related interstitial pneumonia.
RT-PCR is the gold standard  for diagnosis of Covid–19, even if a high false negative rate has been reported. The presence of viral pneumonia is one of the most important features of this infection, and imaging techniques play a role in its diagnostics and management, as they already do for all the viral pneumonia types. The most widespread imaging used for thorax are the CXR, followed by CT, which is more complex to perform in patients with limited mobility, e.g. those in Intensive Care Units (ICU) or those who are isolated at home, and Lung Ultrasonography (LUS), not yet widespread and operator dependent.
From a clinical practice point of view, CXR is easier to perform in ICU and/or at home, during isolation in disease or after discharge, and its diagnostic parameters are very well established and standardized, while LUS  is also easy to do at the bedside and/or at home, but it is still quite operator dependent and needs standardized protocols . Indeed, speaking in terms of “quick response rate”, as rapid progression is a main risk, imaging techniques can be of great advantage to have a constant possibility of bedside monitoring, especially if the use of an automated texture analysis may greatly improve diagnostic usefulness of CXR, giving to the clinician, who can still keep making a qualitative interpretation of the images, an added value of objective, big data based, analysis.
From the availability point of view, CXR, together with LUS, appear to be the most readily available technologies, as the CT needs fixed facilities and patient movement, while PCR needs time and, due to possible local peaks of first diagnosis and contacts screening, may undergo lack of diagnostic kits. Anyway, as PCR and imaging do not explore the same issues, an integration between clinics, laboratory and imaging appears to be the most effective way of management . This is further true considering that asymptomatic patients may have viral pneumonia features  so that a widespread use of imaging may be useful to identify these cases, and CXR with automated image analysis is quick to perform, with limited radiation load, and cost effective.
Finally, from a general Public Health and Big Data in Health perspective, accumulation of CXR images and patients data in large databases, possibly shared at global level, may give the possibility of repeating more in-deep analyses, thus offering the possibility of a continuous, progressive refinement of the knowledge, in a framework of “new light through old windows”, performing longitudinal, retrospective studies to evaluate the evolution of the disease.
Moreover, it is widely expected that a further pandemic peak could occur during the seasonal influenza virus epidemic . In this scenario it is pivotal to quickly discriminate the pneumonia etiology to effectively play out the pandemic containments’ strategies. Lung pathological studies on autoptic tissue from patients with Covid–19 and influenza virus H1N1 pneumonia suggest widespread thrombosis with microangiopathy and vascular angiogenesis are high value discriminators . Texture analysis could improve the CXR image knowledge extraction in this direction.
Several studies limitation should be considered analyzing the reported results. First, images were accessed from a publicly available repository and no additional clinical information was provided. No information related to the temporal interval between the onset of symptoms compared to the CXR acquisition was provided. The number of images of Covid–19 related pneumonia available for the final analysis was relatively low compared to the total number of images (110 out of 219). The non Covid–19 related interstitial pneumonia patients were selected randomly from the entire repository (1345 images). All CXR images were segmented manually, which might not be feasible in the analysis of very large datasets.
However, our high-performance diagnostic results, combined with a robust ensemble machine learning could be considered a proof of concept to test a quick, inexpensive and safe screening system for Covid–19.
The EML model we propose is a more stable alternative to the single classification models, which are much subject to dataset dimension variations, class imbalances, outliers and so on and so forth . Indeed, the EML model performs well in the two extreme cases of data availability: when data sets are small and when data sets are large and unwieldy [24, 25]. Moreover, the EML provide a score that can be easily evaluated to balance the diagnostic performance (optimizing the sensitivity or the specificity or the global accuracy). Furthermore, to the best of our knowledge, texture analysis diagnostic performance has not been tested on CXR during the Covid–19 pandemic.
Despite the several limitations, this study can lay ground for future researches in this field and help developing more accurate diagnostic tools for this disease.