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

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

doi:10.3389/fmolb.2022.836862

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

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

Authors

Na Bai¹, Ruikai Lin¹, Zhiwei Wang², Shengyan Cai³, Jianliang Huang⁴, Zhongrui Su⁴, Yuanzhen Yao⁴, Fang Wen⁵, Han Li¹, Yuxin Huang⁶, Yi Zhao⁶, Tao Xia¹, Mingsheng Lei⁴, Weizhen Yang³, Zhaowen Qiu¹

Affiliations

¹ College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
² China United Network Communications Corporation Heilongjiang Branch, Harbin, China.
³ Hongqi Hospital Affiliated to Mudanjiang Medical University, Mudanjiang, China.
⁴ Zhangjiajie Hospital Affiliated to Hunan Normal University, Zhangjiajie, China.
⁵ Medical College of Jishou University, Jishou, China.
⁶ Heilongjiang Tuomeng Technology Co. Ltd., Harbin, China.

Abstract

Purpose: Computer-aided diagnostic methods were used to compare the characteristics of the Original COVID-19 and its Delta Variant. Methods: 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). Results: 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], p < 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, p = 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, p = 0.012). The left and the right lower lobes contributed most to lung involvement at any given time. Conclusion: 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.

Keywords: Delta Variant; Original COVID-19; chest CT; deep learning; quantitative analysis; stereoscopic segmentation; three-dimensional reconstruction.