Abstract
One of the most challenging aspects related to Covid-19 is to establish the presence of infection in an 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. 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. 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 region 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. 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 by 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. 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 the ground for future research works in this field and help to develop more rapid and accurate screening tools for these patients.
Highlights
Since December 2019, a series of unidentified interstitial pneumonias were reported in the city of Wuhan in the Hubei province, China [1]
Texture analysis of Chest X-Ray images and machine learning algorithms may help in differentiating patients with Covid–19 pneumonia from patient with other pneumonia with a different etiology
The detection of the SARS-CoV–2 have been investigated in different types of clinical specimens such as bronchoalveolar lavage (BAL), sputum, feces and blood revealing that extrapulmonary transmission is possible and suggests a systemic involvement of the infection [6]
Summary
Since December 2019, a series of unidentified interstitial pneumonias were reported in the city of Wuhan in the Hubei province, China [1]. The real-time reverse-transcriptase–polymerase-chain-reaction (RT–PCR) of nasopharyngeal swabs has been used in order to confirm the diagnosis of Covid–19 in patients when respiratory symptoms such as cough, fever or dyspnea on exertion as reported in [4, 5]. Covid–19 diagnosis based on chest imaging showed an higher sensitivity similarity to nasopharyngeal swabs one, with a quick results production [8]. In this scenario, radiological examinations are pivotal for the clinicians, allowing to early diagnose and evaluate the progression of Covid–19 in the emergency frontline
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