Chest X-ray (CXR) is the first tool globally employed to detect cardiopulmonary pathologies. These acquisitions are highly affected by scattered photons due to the large field-of-view required. Scatter in CXRs introduces background in the images, which reduces their contrast. We developed three deep learning-based models to estimate and correct the scatter contribution to CXRs. We used a Monte Carlo (MC) ray-tracing model to simulate CXRs from human models obtained from CT scans using different configurations (depending on the availability of dual-energy acquisitions). The simulated CXRs contained the separated contribution of direct and scattered X-rays in the detector. These simulated datasets were then used as the reference for the supervised training of several NN. Three NN models (single and dual energy) were trained with the MultiResUNet architecture. The performance of the NN models was evaluated on CXRs obtained with the MC code from chest CTs of patients affected by COVID-19. The results showed that the NN models were able to estimate and correct the scatter contribution to CXRs with an error <5%, being robust to variations in the simulation setup and improving the contrast in soft tissue. The single-energy model was tested with real CXRs, providing robust estimations of the scatter-corrected CXRs.

During the COVID-19 pandemic, the high prevalence of comorbidities in Mexico, as well as the disparities between public and private health subsystems, substantially contributed to the severe impact it had in the country. The objective of this study was to evaluate and compare risk factors present at admission for mortality of hospitalized patients with COVID-19. A 2-year retrospective cohort study of hospitalized adult patients with COVID-19 was conducted at a private tertiary care center. The study population consisted of 1,258 patients with a median age of 56 ± 16.5 years, of whom 1,093 recovered (86.8%) and 165 died (13.1%). In the univariate analysis, older age (p <0.001), comorbidities such as hypertension (p <0.001) and diabetes (p <0.001), signs and symptoms of respiratory distress, and markers of acute inflammatory response were significantly more frequent in non-survivors. The multivariate analysis showed that older age (p <0.001), the presence of cyanosis (p 0.005) and previous myocardial infarction (p 0.032) were independent predictors for mortality. In the studied cohort, risk factors present at admission associated with an increased risk of death were older age, cyanosis and a previous myocardial infarction, which can be used as valuable predictors for patients’ outcomes. To our knowledge, this is the first study analyzing predictors of mortality in COVID-19 patients attended on a private tertiary hospital in Mexico.