AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia

Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Alienor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Teodor Grand; Jules Gregory; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stephane Tran Ba; Valerie Bousson; Marie-Pierre Revel; Nikos Paragios

deep-learning to quantify COVID-19 disease extent on CT but none of them used a multi-centric cohort while providing comparisons with segmentations done by radiologists 18, 19 . Disease extent is the only parameter that can be visually estimated on chest CT to quantify disease severity 4, 5 , but visual quantification is difficult and usually coarse. Several AI-based tools have been recently developed to quantify interstitial lung diseases (ILD) [20] [21] [22] [23] , which share common CT features with COVID-19 pneumonia, especially a predominance of ground glass opacities. In this study, we investigated a fully automatic method ( Figure 1 ) for disease quantification, staging and short-term prognosis. The approach relied on (i) a disease quantification solution that exploited 2D & 3D convolutional neural networks using an ensemble method, (ii) a biomarker discovery approach sought to determine the share space of features that are the most informative for staging & prognosis, & (iii) an ensemble robust supervised classification method to distinguish patients with severe vs non-severe short-term outcome and among severe patients those intubated and those who did not survive.