COVID-19 was declared a pandemic by WHO on March 11. Since then, there have been 8.15 million confirmed cases worldwide with a case fatality rate ranging from 16.3% to 0.1%. In the US, there have been 2,187,202 cases with a 5.4% case fatality rate as of June 16, 2020. The magnitude of this infectious disease has stressed the need to develop novel methodologies to define who are at the highest risk of developing acute symptoms. X-Ray (CXR) and Computed Tomography (CT) play a fundamental role in the detection and follow-up of the COVID-19 lung injury. It also provides a unique opportunity to define quantitative biomarkers that may identify susceptible subjects to the acute phase of the disease using pre-infection and early infection radiological exams. This proposal's broad objective is to provide a better understanding of acute COVID-19 susceptibility markers based on artificial intelligence approaches on radiological exams, both CT and CXR. CT offers a unique way to phenotype the lung and its changes. Subtle changes of normal parenchyma have been associated with systemic inflammation that can be detected on CT. We hypothesize that susceptible subjects for acute COVID- 19 disease evolution will express inflamed normal parenchymal signatures that can be measured on CT scan prior to the infection or in the early phases of the viral infection. We will develop new computational approaches to identify radiographic patterns consistent with inflamed normal parenchyma as well as early COVID-19 injury and compute radiomics signature that can capture the heterogeneity of the radiographic expression for each lung pattern. We will define new CT-based biomarkers for acute COVID-19 susceptibility using Gradient Boosting decision trees and feature importance. We will then translate the quantification of the most relevant features in CXR image using image translation approaches based on deep neural networks. Finally, we will integrate these automated tools in the CIP workstation using clinically friendly end-to-end workflows to empower clinical investigations across the world. We will continue the support and dissemination of this tool across the research community. Over the last 15 years, our group has developed the Chest Imaging Platform (CIP), an NIH-funded open-source software tool for the automated phenotyping of chest CT scans that is widely used in the chronic lung disease research community. Since the beginning of the pandemic, CIP has been used to the characterization of COVID-19 using existing densitometric metrics. Our commitment to open science in the form of open toolkits that are freely distributed is fundamental to catalyze the application of AI and imaging in the context of this pandemic.