Since its outbreak in December 2019, the persistent coronavirus disease (COVID-19) became a global health emergency. It is imperative to develop a prognostic tool to identify high-risk patients and assist in the formulation of treatment plans. We retrospectively collected 366 severe or critical COVID-19 patients from four centers, including 70 patients who died within 14 days (labeled as high-risk patients) since their initial CT scan and 296 who survived more than 14 days or were cured (labeled as low-risk patients). We developed a 3D densely connected convolutional neural network (termed De-COVID19-Net) to predict the probability of COVID-19 patients belonging to the high-risk or low-risk group, combining CT and clinical information. The area under the curve (AUC) and other evaluation techniques were used to assess our model. The De-COVID19-Net yielded an AUC of 0.952 (95% confidence interval, 0.928-0.977) on the training set and 0.943 (0.904-0.981) on the test set. The stratified analyses indicated that our model's performance is independent of age, sex, and with/without chronic diseases. The Kaplan-Meier analysis revealed that our model could significantly categorize patients into high-risk and low-risk groups ( p < 0.001). In conclusion, De-COVID19-Net can non-invasively predict whether a patient will die shortly based on the patient's initial CT scan with an impressive performance, which indicated that it could be used as a potential prognosis tool to alert high-risk patients and intervene in advance.

中文翻译：

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深度学习预后模型有助于对死亡高风险的 COVID-19 患者发出警报：一项多中心研究

自 2019 年 12 月爆发以来，持续性冠状病毒病 (COVID-19) 已成为全球卫生紧急事件。开发一种预后工具来识别高危患者并协助制定治疗计划势在必行。我们回顾性收集了来自四个中心的 366 名重症或危重症 COVID-19 患者，其中 70 名患者自初次 CT 扫描后 14 天内死亡（标记为高危患者），296 名存活超过 14 天或治愈的患者（标记为高危患者）。低风险患者）。我们开发了一种 3D 密集连接的卷积神经网络（称为 De-COVID19-Net），结合 CT 和临床信息来预测 COVID-19 患者属于高风险或低风险组的概率。使用曲线下面积（AUC）和其他评估技术来评估我们的模型。De-COVID19-Net 在训练集上的 AUC 为 0.952（95% 置信区间，0.928-0.977），在测试集上的 AUC 为 0.943（0.904-0.981）。分层分析表明，我们的模型的表现与年龄、性别以及是否患有慢性疾病无关。Kaplan-Meier 分析表明，我们的模型可以显着地将患者分为高风险组和低风险组（ p< 0.001）。总之，De-COVID19-Net可以根据患者的初始CT扫描无创地预测患者是否会在短期内死亡，表现令人印象深刻，这表明它可以作为潜在的预后工具来提醒高危患者和提前干预。