Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.
Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.
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DOI：
10.1038/s41598-020-76282-0
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发表时间：
2020-11-05
影响因子：
4.6
通讯作者：
Yu H
中科院分区：
文献类型：
--
作者：
Chen J;Wu L;Zhang J;Zhang L;Gong D;Zhao Y;Chen Q;Huang S;Yang M;Yang X;Hu S;Wang Y;Hu X;Zheng B;Zhang K;Wu H;Dong Z;Xu Y;Zhu Y;Chen X;Zhang M;Yu L;Cheng F;Yu H
计算机断层扫描（CT）是诊断2019新型冠状病毒（COVID 19）肺炎的首选影像学方法。我们旨在构建一个基于深度学习的系统，用于在高分辨率CT上检测COVID-19肺炎。为了模型开发和验证，回顾性收集了武汉大学人民医院106名住院患者的46，096张匿名图像，其中包括51名实验室确诊的COVID-19肺炎患者和55名其他疾病的对照患者。在武汉大学人民医院收集了27例前瞻性连续患者，以评估放射科医生对2019-CoV肺炎的效率。在潜江市中心医院进行了外部测试，以评估系统的鲁棒性。该模型在内部回顾性数据集中实现了95.24%的患者准确率和98.85%的图像准确率。对于27名内部前瞻性患者，该系统实现了与放射科专家相当的性能。在外部数据集上，它达到了96%的准确率。在该模型的辅助下，放射科医师的阅读时间大大减少了65%。深度学习模型表现出与放射科专家相当的性能，大大提高了放射科医生在临床实践中的效率。
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
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