【摘要】 实时逆转录聚合酶链反应(RT-PCR)检测早期新冠病毒肺炎(COVID-19)患者的痰液或鼻咽拭子中的病毒RNA阳性率较低。同时,COVID-19的计算机断层扫描(CT)影像学的临床表现有其自身的特点,不同于甲型流感病毒性肺炎(IAVP)等其他类型的病毒性肺炎。本研究旨在应用深度学习技术,建立COVID-19、IAVP及健康人群肺部CT的早期筛查模型。本研究共采集618份CT样本,其中219份样本来自110例COVID-19患者(平均年龄50岁,其中男性63例,占57.3%),224份样本来自224例IAVP患者(平均年龄61岁,其中男性156例,占69.6%),175份样本来自健康人群(平均年龄39岁,其中男性97例,占55.4%)。所有CT样本均来自浙江省三家COVID-19定点收治医院。我们首先利用胸部CT图像集的三维(3D)深度学习模型分割出候选感染区域,然后利用位置敏感机制深度学习网络将这些分离的图像归类为COVID-19、IAVP以及与感染无关(ITI)的图像,并且输出相应置信度得分。最后,用Noisy-OR贝叶斯函数计算每份CT病例的感染类型及总置信度。测试数据集的实验结果表明,从整体CT病例来看,本研究利用深度学习系统建立的COVID-19患者的早期筛查模型的总体准确率为86.7%。该模型有望成为一线临床医生诊断COVID-19的一种有效的辅助方法。更多还原

【Abstract】 The real-time reverse transcription-polymerase chain reaction(RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab had a relatively low positive rate in the early stage of coronavirus disease2019(COVID-19). Meanwhile, the manifestations of COVID-19 as seen through computed tomography(CT) imaging show individual characteristics that differ from those of other types of viral pneumonia such as influenza-A viral pneumonia(IAVP). This study aimed to establish an early screening model to distinguish COVID-19 from IAVP and healthy cases through pulmonary CT images using deep learning techniques. A total of 618 CT samples were collected: 219 samples from 110 patients with COVID-19(mean age 50 years; 63(57.3%) male patients); 224 samples from 224 patients with IAVP(mean age 61 years;156(69.6%) male patients); and 175 samples from 175 healthy cases(mean age 39 years; 97(55.4%) male patients). All CT samples were contributed from three COVID-19-designated hospitals in Zhejiang Province, China. First, the candidate infection regions were segmented out from the pulmonary CT image set using a 3 D deep learning model. These separated images were then categorized into the COVID-19,IAVP, and irrelevant to infection(ITI) groups, together with the corresponding confidence scores, using a location-attention classification model. Finally, the infection type and overall confidence score for each CT case were calculated using the Noisy-OR Bayesian function. The experimental result of the benchmark dataset showed that the overall accuracy rate was 86.7% in terms of all the CT cases taken together. The deep learning models established in this study were effective for the early screening of COVID-19 patients and were demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.更多还原