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

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.

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Copyright © 2022 Guiot, Maes, Winandy, Henket, Ernst, Thys, Frix, Morimont, Rousseau, Canivet, Louis, Misset, Meunier, Charbonnier and Lambermont.

Déclaration de conflit d'intérêts

J-PC was employed by Thirona. JG reports personal fees for advisory board, work and lectures from Boehringer Ingelheim, Janssens, GSK, Roche, and Chiesi, non-financial support for meeting attendance from Chiesi, Roche, Boehringer Ingelheim, and Janssens. He is in the permanent SAB of Radiomics (Oncoradiomics SA) for the SALMON trial without any specific consultancy fee for this study. He is co-inventor of one issued patent on radiomics licensed to radiomics (Oncoradiomics SA). He confirms that none of the above entities of funding was involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication. The remaining authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as the potential conflicts of interest.