Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach./nMethods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction./nResults: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians./nConclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.

中文翻译：

---

用于诊断 COVID-19 的快速筛查分类器


理由： 2019 年冠状病毒病 (COVID-19) 已引起全球大流行。将胸部 X 光 (CXR) 与临床特征相结合的分类器可作为一种快速筛查方法。/n 方法：该研究纳入了 512 名 COVID-19 患者和 106 名甲型/乙型流感肺炎患者。应用深度神经网络（DNN），从 CXR 和临床结果衍生的深度特征形成融合特征以进行诊断预测。/n结果： COVID-19 和流感的临床特征表现出不同的模式。 COVID-19 患者发烧较少，腹泻较多，高凝状态更为明显。使用临床特征或 CXR 构建的分类器的受试者工作曲线下面积 (AUC) 分别为 0.909 和 0.919。结合临床特征和CXR的分类器的诊断效能显着提高，AUC为0.952，敏感性为91.5%，特异性为81.2%。此外，组合分类器在重症和非服务性 COVID-19 中均有效，其 AUC 为 0.971，在非重症病例中的敏感性为 96.9%，与基于计算机断层扫描 (CT) 的分类器相当，但相对而言与CT相比，严重病例的疗效较差。此外，我们还进行了一项涉及三名经验丰富的肺科医师的读者研究，与经验丰富的肺科医师相比，人工智能 (AI) 系统在周转时间和诊断准确性方面表现出优越性。/n 结论：使用临床和 CXR 特征构建的分类器是有效的经济、辐射安全，可用于区分 COVID-19 和甲型/乙型流感肺炎，是 COVID-19 大流行期间理想的快速筛查工具。