摘要
目的:探讨新型冠状病毒肺炎(COVID-19)各临床分型的CT表现,分析人工智能辅助技术的应用价值。方法:结合"uAI新冠肺炎智能辅助分析系统"回顾性分析2020年1月23日至2020年2月25日收治的临床及CT资料完整的44例COVID-19病例。结果:①CT表现临床轻型患者4例,胸部CT呈阴性。临床普通型患者32例,CT表现为单侧或双肺多发病灶,呈片样或楔形样磨玻璃影,其内可见血管及支气管穿行,常伴有小叶间隔增厚,可见"铺路石"征及支气管充气征,部分病变内部可见小范围肺实变。临床重型/危重型病例共8例,CT表现为病变范围广泛,磨玻璃影、实变及纤维条索混杂,多伴有"铺路石"征、支气管充气征。②人工智能辅助诊断软件病灶标记范围与肉眼观察相比具有较好的一致性。4例轻型患者病灶总体积显示为零。32例临床普通型患者病灶总体积为(109.9±94.9)cm^3,病灶内部磨玻璃影体积为(55.3±50.4)cm^3,实变体积为(34.9±35.0)cm^3。8例重型/危重型患者病灶总体积为(858.1±351.9)cm^3,病灶内部磨玻璃影体积(486.7±204.0)cm^3,实变体积(204.1±119.3)cm^3。普通型、重型/危重型患者的病灶总体积、内部磨玻璃体积及实变体积组间的差异有显著统计学意义(P <0.001)。以人工智能辅助诊断软件提供的各肺叶病灶占比进行统计,临床普通型患者肺损伤评分为(3.8±1.2)分,重型/危重型患者肺损伤评分为(10.4±5.1)分,二者之间的差异有统计学意义(P <0.05)。ROC曲线显示以5.5分为肺损伤界值时,预测临床分型的效能最高,ROC曲线下面积为0.996。结论:COVID-19的CT表现为多发的肺外围或胸膜下的磨玻璃影,其内含正常走行的血管及支气管,病变范围增大、增多或实变代表病情进展。人工智能辅助技术可有效识别COVID-19病灶,提供病灶总体积、内部磨玻璃影及实变体积相关数据,对患者肺损伤程度进行准确量化评分,为临床预后评估提供帮助,同时提高影像医师的工作效率。
Objective:To investigate the novel coronavirus pneumonia clinical manifestation of CT and analyze the application value of AI.Methods:A retrospective analysis of 44 cases of the novel coronavirus pneumonia with complete clinical and CT data from January 23,2020 to February 25,2020 with the help of"uAI novel Coronavirus Pneumonia Intelligent Assisted Analysis System".Results:1.4 cases were clinically mild,and chest CT showed negative findings.In 32 cases of clinical common type,CT showed multiple lesions of one or two lungs,which were flake like or wedge-shaped ground glass shadow,in which blood vessels and bronchi passing through.Most of them accompanied by interlobular septal thickening,paving stone sign and bronchi inflation sign,and in some lesions,there were small round lung consolidation.There were 8 cases of clinical severe and critical severe cases.CT showed a wide range of lesions,ground glass shadow,consolidation and fiber cord mixed,most of them were accompanied with"paving stone"sign and broncho inflation sign.2.Compared with the visual inspection,the scope of the lesions in the artificial intelligence aided diagnosis software has a better consistency.The total lesion volume was shown to be 0 in 4 mild patients.The total lesion volume of 32 patients with clinical common type was(109.9±94.9)cm^3.The volume of ground glass shadow in the lesion was(55.3±50.4)cm^3,and the lung consolidation volume was(34.9±35.0)cm^3.The total lesion volume of 8 patients with clinical Severe and critically type was(858.1±351.9)cm^3.The volume of ground glass shadow in the lesion was(486.7±204.0)cm^3.and the lung consolidation volume was(204.1±119.3)cm^3.There was significant difference in the total volume of lesions,the volume of internal ground glass and the volume of consolidation between the general and severe/critical patients.The proportion of lesions in each lung lobe provided by artificial intelligence aided diagnosis software was statistically analyzed.The score of lung injury in clinical common type patients was(3.8±1.2)points,and that in severe/critical type patients was(10.4±5.1)points.The difference between the two was statistically significant(P<0.05).ROC curve showed that when the lung injury threshold was 5.5,the prediction of clinical classification was the highest,and the area under ROC curve was 0.996.Conclusion:CT scan of novel coronavirus pneumonia showed multiple ground glass opacities in the periphery of lung or under pleura,which contained normal vessels and bronchus.The enlarged,increased or consolidated lesions represented the progress of the disease.AI-assisted technology can effectively identify novel coronavirus pneumonia lesions,provide data related to total lesion volume,internal ground glass shadow and real volume,accurately quantify the degree of lung injury,provide help for clinical condition assessment,and improve the work efficiency of imaging physicians.
作者
王增奎
张兆福
庞军
魏晓华
庞红艳
郜东伟
WANG Zengkui;ZHANG Zhaofu;PANG Jun;WEI Xiaohua;PANG Hongyan;GAO Dongwei(Medical Imaging Center,Cangzhou People’s Hospital,Cangzhou 061000,China;Respiratory critical care department,Cangzhou People’s Hospital,Cangzhou 061000,China)
出处
《CT理论与应用研究（中英文）》
2020年第5期534-542,共9页
Computerized Tomography Theory and Applications
基金
沧州市重点研发计划指导项目(No.201101006)。
