ABSTRACT
目的 探讨2019冠状病毒病（COVID-19）患者病程中胸部CT影像学表现的变化规律。 方法 收集在浙江大学医学院附属第一医院集中收治的COVID-19确诊患者52例。所有患者病程中持续胸部CT复查，人均共行4次胸部CT检查，每次检查间隔时间2~7 d。回顾性分析诊疗过程中患者CT影像学特点及随时间变化的特点。 结果 除2例患者首次胸部CT影像无异常，其余50例患者均发现肺部有不同程度的阴影。其中，表现为磨玻璃样密度影（GGO）48例（92.3%），斑片状实变、亚实变19例（36.5%），17例（32.7%）伴随出现空气支气管征，小叶间隔增粗41例（78.8%）。病程中COVID-19患者肺部GGO病变逐渐减少，纤维索条影逐渐增多，成为最常见的影像学表现。39例患者（75.0%）在入院第6~9天肺部病灶变化最明显，在入院第10~14天40例（76.9%）患者肺部病灶明显吸收。 结论 COVID-19患者的胸部CT影像学表现具有一定的特征和变化规律，这在疫情防控和临床治疗决策中具有较大价值。
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 disease 2019 (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 3D 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.