Affectation index and severity degree by COVID-19 in Chest X-ray images using artificial intelligence

Abstract

The Covid-19 pandemic has caused the congestion of intensive therapies making it impossible for each to have a full-time radiology service. An indicator is necessary to allow intensivists to evaluate the evolution of patients in advanced state of the disease depending on the degree of involvement of their lungs and their severity in chest X-ray images (CXR). We propose an algorithm to grade the affectation of lungs in CXR images in patients diagnosed with COVID-19 in advanced state of the disease. The algorithm combines the assessment of image quality, digital image processing and deep learning for segmentation of the lung tissues and their classification. The proposed segmentation method is capable of dealing with the problem of diffuse lung borders in CXR images of patients with COVID-19 severe or critical. The calculation of the affectation index (IAF) consists of the classification of the segmented image by establishing the relationship between the number of pixels of each class. The IAF index of lung affectation in CXR images and the algorithm for its calculation. A correlation was established between the IAF and the international classification of the degree of severity established by radiologists.