Imaging Department, Gustave Roussy, Université Paris -Saclay, Villejuif, 94805, France.
Biomaps, UMR 1281 INSERM, CEA, CNRS, Université Paris-Saclay, Villejuif, 94805, France.
Nat Commun. 2021 Jan 27;12(1):634. doi: 10.1038/s41467-020-20657-4.
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.
SARS-COV-2 大流行给重症监护病房带来了压力，因此确定疾病严重程度的预测因素是当务之急。我们从法国的两家医院收集了 1003 名冠状病毒感染患者的 58 个临床和生物学变量以及胸部 CT 扫描数据。我们基于 CT 扫描训练了一个深度学习模型来预测严重程度。然后，我们构建了多模态 AI 严重程度评分，该评分除了深度学习模型外，还包括 5 个临床和生物学变量（年龄、性别、氧合、尿素、血小板）。我们表明，尽管 CT 扫描的神经网络分析与其他严重程度标志物（氧合、LDH 和 CRP）相关，但它提供了独特的预后信息，解释了当将 CT 扫描信息添加到临床变量时 AUC 仅增加了 0.03。在这里，我们表明，当将 AI 严重程度与 11 种现有的严重程度评分进行比较时，我们发现预后性能有了显著提高；因此，AI 严重程度可以迅速成为一种参考评分方法。
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