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探索新特征:利用深度学习和三维重建技术基于胸部CT比较原始新冠病毒及其德尔塔变种

Exploring New Characteristics: Using Deep Learning and 3D Reconstruction to Compare the Original COVID-19 and Its Delta Variant Based on Chest CT.

作者信息

Bai Na, Lin Ruikai, Wang Zhiwei, Cai Shengyan, Huang Jianliang, Su Zhongrui, Yao Yuanzhen, Wen Fang, Li Han, Huang Yuxin, Zhao Yi, Xia Tao, Lei Mingsheng, Yang Weizhen, Qiu Zhaowen

机构信息

College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.

China United Network Communications Corporation Heilongjiang Branch, Harbin, China.

出版信息

Front Mol Biosci. 2022 Mar 11;9:836862. doi: 10.3389/fmolb.2022.836862. eCollection 2022.

Abstract

Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant. This was a retrospective study. A deep learning segmentation model was applied to segment lungs and infections in CT. Three-dimensional (3D) reconstruction was used to create 3D models of the patient's lungs and infections. A stereoscopic segmentation method was proposed, which can subdivide the 3D lung into five lobes and 18 segments. An expert-based CT scoring system was improved and artificial intelligence was used to automatically score instead of visual score. Non-linear regression and quantitative analysis were used to analyze the dynamic changes in the percentages of infection (POI). The POI in the five lung lobes of all patients were calculated and converted into CT scores. The CT scores of Original COVID-19 patients and Delta Variant patients since the onset of initial symptoms were fitted over time, respectively. The peak was found to occur on day 11 in Original COVID-19 patients and on day 15 in Delta Variant patients. The time course of lung changes in CT of Delta Variant patients was redetermined as early stage (0-3 days), progressive and peak stage (4-16 days), and absorption stage (17-42 days). The first RT-PCR negative time in Original COVID-19 patients appeared earlier than in Delta Variant patients (22 [17-30] vs. 39 [31-44], < 0.001). Delta Variant patients had more re-detectable positive RT-PCR test results than Original COVID-19 patients after the first negative RT-PCR time (30.5% vs. 17.1%). In the early stage, CT scores in the right lower lobe were significantly different (Delta Variant vs. Original COVID-19, 0.8 ± 0.6 vs. 1.3 ± 0.6, = 0.039). In the absorption stage, CT scores of the right middle lobes were significantly different (Delta Variant vs. Original COVID-19, 0.6 ± 0.7 vs. 0.3 ± 0.4, = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time. Compared with the Original COVID-19, the Delta Variant has a longer lung change duration, more re-detectable positive RT-PCR test results, different locations of pneumonia, and more lesions in the early stage, and the peak of infection occurred later.

摘要

采用计算机辅助诊断方法比较原始新冠病毒及其德尔塔变异株的特征。这是一项回顾性研究。应用深度学习分割模型对CT中的肺部和感染区域进行分割。使用三维(3D)重建技术创建患者肺部和感染区域的3D模型。提出了一种立体分割方法,可将3D肺部分为五个肺叶和18个肺段。改进了基于专家的CT评分系统,并使用人工智能进行自动评分而非视觉评分。采用非线性回归和定量分析方法分析感染百分比(POI)的动态变化。计算所有患者五个肺叶的POI并转换为CT评分。分别拟合原始新冠病毒患者和德尔塔变异株患者自初始症状出现以来的CT评分随时间的变化。发现原始新冠病毒患者的峰值出现在第11天,德尔塔变异株患者的峰值出现在第15天。将德尔塔变异株患者CT肺部变化的时间进程重新确定为早期(0 - 3天)、进展期和高峰期(4 - 16天)以及吸收期(17 - 42天)。原始新冠病毒患者首次RT-PCR阴性时间比德尔塔变异株患者出现得更早(22 [17 - 30] 天对39 [31 - 44] 天,<0.001)。德尔塔变异株患者在首次RT-PCR阴性时间后可再次检测到阳性RT-PCR检测结果的比例高于原始新冠病毒患者(30.5%对17.1%)。在早期,右下叶的CT评分有显著差异(德尔塔变异株对原始新冠病毒,0.8±0.6对1.3±0.6,P = 0.039)。在吸收期,右中叶的CT评分有显著差异(德尔塔变异株对原始新冠病毒,0.6±0.7对0.3±0.4,P = 0.012)。在任何给定时间,左肺下叶和右肺下叶对肺部受累的贡献最大。与原始新冠病毒相比,德尔塔变异株的肺部变化持续时间更长,可再次检测到阳性RT-PCR检测结果的比例更高,肺炎位置不同,早期病变更多,且感染峰值出现较晚。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/819b/8961806/de0661d6bb7d/fmolb-09-836862-g001.jpg

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