Kikkisetti Shreeja, Zhu Jocelyn, Shen Beiyi, Li Haifang, Duong Tim Q
Radiology, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, NY, USA.
Radiology, State University of New York at Stony Brook, Stony Brook, NY, USA.
PeerJ. 2020 Nov 5;8:e10309. doi: 10.7717/peerj.10309. eCollection 2020.
Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia ( = 455) on pCXR from normal ( = 532), bacterial pneumonia ( = 492), and non-COVID viral pneumonia ( = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.
便携式胸部X光（pCXR）已成为2019冠状病毒病（COVID-19）肺部感染管理中不可或缺的工具。本研究采用深度学习卷积神经网络，对pCXR上的COVID-19肺部感染与正常及相关肺部感染进行分类，以实现更及时、准确的诊断。这项回顾性研究采用具有迁移学习的深度学习卷积神经网络（CNN），根据pCXR将COVID-19肺炎（n = 455）与正常情况（n = 532）、细菌性肺炎（n = 492）和非COVID病毒性肺炎（n = 552）进行分类。数据被随机分为75%用于训练和25%用于测试。对测试集分别使用五折交叉验证。使用受试者工作特征曲线分析评估性能。将其与在整个pCXR和分割肺部上运行的CNN进行比较。在多类模型中，CNN能准确地将COVID-19的pCXR与正常、细菌性肺炎和非COVID-19病毒性肺炎患者的pCXR区分开来。总体敏感性、特异性、准确性和AUC分别为0.79、0.93、0.79和0.85（整个pCXR），以及0.91、0.93、0.88和0.89（分割肺部的CXR）。使用分割肺部时性能通常更好。热图显示，CNN能准确地在pCXR上定位模糊外观、磨玻璃影和/或实变区域。具有迁移学习的深度学习卷积神经网络能在便携式胸部X光上准确地将COVID-19与正常、细菌性肺炎或非COVID病毒性肺炎区分开来。这种方法有可能通过提供更及时、准确的诊断来帮助放射科医生和一线医生。
