An Explainable AI System for Automated COVID-19 Assessment and Lesion Categorization from CT-scans

COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic outbreak all over the world with exponential increasing of conrmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We rst propose a new segmentation module aimed at identifying automatically lung parenchyma and lobes. Next, we combined such segmentation network with classication networks for COVID-19 identication and lesion categorization. We compare the obtained classification results with those obtained by three expert radiologists on a dataset consisting of 162 CT scans. Results showed a sensitivity of 90% and a specicity of 93.5% for COVID-19 detection, outperforming those yielded by the expert radiologists, and an average lesion categorization accuracy of over 84%. Results also show that a signicant role is played by prior lung and lobe segmentation that allowed us to enhance performance by over 20 percent points. The interpretation of the trained AI models, moreover, reveals that the most signicant areas for supporting the decision on COVID-19 identification are consistent with the lesions clinically associated to the virus, i.e., crazy paving, consolidation and ground glass. This means that the articial models are able to discriminate a positive patient from a negative one (both controls and patients with interstitial pneumonia tested negative to COVID) by evaluating the presence of those lesions into CT scans. Finally, the AI models are integrated into a user-friendly GUI to support AI explainability for radiologists, which is publicly available at http: // perceivelab. com/ covid-ai . The whole AI system is unique since, to the best of our knowledge, it is the first AI-based software, publicly available, that attempts to explain to radiologists what information is used by AI methods for making decision and that involves proactively them in the decision loop to further improve the COVID-19 understanding.