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 staging/prognosis was implemented using a hierarchical classification principle, targeting first staging and subsequently prognosis. The staging component sought to separate patients with severe and non-severe short-term outcomes, while the prognosis sought to predict the risk of decease among severe patients. On the basis of the feature selection step, the machine learning algorithms that had a balanced accuracy greater than 60% on validation were considered. The selection of these methods was done on the basis of minimum discrepancy between performance on training and internal validation sub-training data set. We have built two sequential classifiers using this ensemble method, one to determine the severe cases and a second to predict survival. The classifier aiming to separate patients with severe and non-severe short-term outcomes had a balanced accuracy of 74%, a weighted precision of 79%, a weighted sensitivity of 69% and specificity of 79% to predict a severe short-term outcome ( Figure 4 , Table 6 ). The performance of the second classifier aiming to differentiate between intubated and deceased patients was even higher with a balanced accuracy of 81% ( Figure 4 , Table 7 ). The hierarchical classifiers combing the 3 classes had a balanced accuracy of 68%, a weighted precision of 79%, a weighted sensitivity of 67% and specificity of 83% ( Figure 4) . It was observed that prognosis performance difference between training and external cohort testing was low, suggesting that the most important information present at CT scans was recovered, and additional information should be integrated in order to fully explain the outcome. been shown to be low, such as 63% when perform on nasal swab 28 , chest CT has been shown to provide higher sensitivity for diagnosis of COVID-19 as compared with initial RT-PCR from pharyngeal swab samples 10 . The current COVID-19 pandemic requires implementation of rapid clinical triage in healthcare facilities to categorize patients into different urgency categories 29 , often occurring in the context of limited access to biological tests. Beyond the diagnostic value of CT for COVID-19, our study suggests that AI should be part of the triage process. The developed tool will be made publicly available. Our prognosis and staging method achieved state of the art results through the deployment of a highly robust ensemble classification strategy with automatic feature selection of imaging biomarkers and patients' characteristics available within the image' metadata. In terms of future work, the continuous enrichment of the data base with new examples is a necessary action on top of updating the outcome of patients included in the study. The integration of non-imaging data and other related clinical and categorical variables such as lymphopenia, the D-dimer level and other comorbidities 9, 30-32 is a necessity towards better understanding the disease and predicting the outcomes. This is clearly demonstrated from the inability of any of the state-of-the art classification methods (including neural networks and multi-layer perceptron models) to predict the outcome with a balanced accuracy greater to 80% on the training data. Our findings could have a strong impact in terms of (i) patient stratification with respect to the different therapeutic strategies, (ii) accelerated drug development through rapid, reproducible and quantified assessment of treatment response through the different mid/end-points of the trial