Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.
Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.
复制标题
DOI：
10.1038/s41467-020-20657-4
复制
发表时间：
2021-01-27
影响因子：
16.6
通讯作者：
Blum MGB
中科院分区：
文献类型：
--
作者：
Lassau N;Ammari S;Chouzenoux E;Gortais H;Herent P;Devilder M;Soliman S;Meyrignac O;Talabard MP;Lamarque JP;Dubois R;Loiseau N;Trichelair P;Bendjebbar E;Garcia G;Balleyguier C;Merad M;Stoclin A;Jegou S;Griscelli F;Tetelboum N;Li Y;Verma S;Terris M;Dardouri T;Gupta K;Neacsu A;Chemouni F;Sefta M;Jehanno P;Bousaid I;Boursin Y;Planchet E;Azoulay M;Dachary J;Brulport F;Gonzalez A;Dehaene O;Schiratti JB;Schutte K;Pesquet JC;Talbot H;Pronier E;Wainrib G;Clozel T;Barlesi F;Bellin MF;Blum MGB
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。我们从两家法国医院的1003名冠状病毒感染患者中收集了58个临床和生物学变量以及胸部CT扫描数据。我们训练了一个基于CT扫描的深度学习模型来预测严重程度。然后，我们构建了多模式AI严重程度评分，除了深度学习模型之外，还包括5个临床和生物学变量（年龄，性别，氧合，尿素，血小板）。我们发现，CT扫描的神经网络分析带来了独特的预后信息，尽管它与其他严重程度标志物（氧合，LDH和CRP）相关，解释了将CT扫描信息添加到临床变量时获得的AUC可测量但有限的0.03增加。在这里，我们表明，当将AI严重程度与11个现有的严重程度评分进行比较时，我们发现预后性能显着改善;因此，AI严重程度可以迅速成为一种参考评分方法。SARS-COV-2大流行给重症监护病房带来了压力，因此早期预测严重恶化是一个优先事项。在这里，作者开发了一种多模式严重程度评分，包括临床和成像特征，与以前的评分相比，在两个验证数据集中显著改善了预后性能。
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.
登录
查看更多内容
影响因子：
5.9
作者：
Li, Yan;Yang, Zhenlu;Xia, Liming
通讯作者：
Xia, Liming
使用深度学习对重症 COVID-19 患者进行早期分诊
影响因子：
16.6
作者：
Liang, Wenhua;Yao, Jianhua;He, Jianxing
通讯作者：
He, Jianxing
影响因子：
28.2
作者：
Dai, Mengyuan;Liu, Dianbo;Cai, Hongbing
通讯作者：
Cai, Hongbing
影响因子：
16.6
作者：
Feng Z;Yu Q;Yao S;Luo L;Zhou W;Mao X;Li J;Duan J;Yan Z;Yang M;Tan H;Ma M;Li T;Yi D;Mi Z;Zhao H;Jiang Y;He Z;Li H;Nie W;Liu Y;Zhao J;Luo M;Liu X;Rong P;Wang W
通讯作者：
Wang W
影响因子：
11.8
作者：
Ji, Dong;Zhang, Dawei;Qin, Enqiang
通讯作者：
Qin, Enqiang