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

The classifiers were applied in a hierarchical way, performing first the staging and then the prognosis. More specifically, a majority voting method was applied to classify patients into severe and non-severe cases (Table 6 ). Then, another majority voting was applied on the cases predicted as severe only to classify them into intubated or deceased (Table 7 ). In such a setup, the correlation of the reported features are summarized in Table 5 . For the hierarchical prognosis on the three classes a voting classifier for the prediction of each class against the others has been applied to aggregate the predicted outcomes from the 7 selected methods. In the Figure 8 we visualize the distributions of the different features along the ground truth labels and the prediction of the hierarchical classifier for each subject. In particular, all the samples are grouped using their ground truth labels and a boxplot is generated for each group and each feature. Additionally, color coded points are over imposed at each boxplot denoting the prediction label. It is therefore clearly visible that some features such as the disease extent, the age, the shape of the disease and the uniformity seems to be very important on separating the different subjects.