AUTHOR=Guiot Julien , Maes Nathalie , Winandy Marie , Henket Monique , Ernst Benoit , Thys Marie , Frix Anne-Noelle , Morimont Philippe , Rousseau Anne-Françoise , Canivet Perrine , Louis Renaud , Misset Benoît , Meunier Paul , Charbonnier Jean-Paul , Lambermont Bernard TITLE=Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity JOURNAL=Frontiers in Medicine VOLUME=9 YEAR=2022 URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2022.930055 DOI=10.3389/fmed.2022.930055 ISSN=2296-858X ABSTRACT=

The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.