The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach. The SARS-COV-2 pandemic has put pressure on intensive care units, so that predicting severe deterioration early is a priority. Here, the authors develop a multimodal severity score including clinical and imaging features that has significantly improved prognostic performance in two validation datasets compared to previous scores.
【저자키워드】 viral infection, Risk factors, machine learning, 【초록키워드】 pandemic, Prognosis, deep learning, SARS-CoV-2 pandemic, severity, disease severity, LDH, Sex, CRP, hospitals, Deterioration, intensive care units, CT scan, Patient, Platelet, network analysis, age, dataset, CT scans, predictor, prognostic, information, predict, CT-scan, pressure, marker, chest CT scan, AUC, Oxygenation, urea, CT-scans, clinical variables, priority, French, variable, approach, feature, severity scores, develop, significantly, include, addition, unique, correlated, variables, 【제목키워드】 deep learning, severity of COVID-19, Patient, predict, clinical variable,