Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity

Julien Guiot; Nathalie Maes; Marie Winandy; Monique Henket; Benoit Ernst; Marie Thys; Anne-Noelle Frix; Philippe Morimont; Anne-Françoise Rousseau; Perrine Canivet; Renaud Louis; Benoît Misset; Paul Meunier; Jean-Paul Charbonnier; Bernard Lambermont

doi:10.3389/fmed.2022.930055

Automatized lung disease quantification in patients with COVID-19 as a predictive tool to assess hospitalization severity

Front Med (Lausanne). 2022 Aug 29:9:930055. doi: 10.3389/fmed.2022.930055. eCollection 2022.

Authors

Affiliations

¹ Respiratory Department, University Hospital of Liège, Liège, Belgium.
² Biostatistics and Medico-Economic Information Department, University Hospital of Liège, Liège, Belgium.
³ Intensive Care Department, University Hospital of Liège, Liège, Belgium.
⁴ Department of Radiology, University Hospital of Liège, Liège, Belgium.
⁵ Thirona B.v., Nijmegen, Netherlands.

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.

Keywords: COVID-19; CT scan analysis; ICU length of stay; SARS-CoV-2; artificial intelligence; in-hospital death; mechanical ventilation risk; severity of hospital stay prediction.