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Volume 32 Issue 5
Sep.  2023
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ZHANG K, CHAI J, LIU R, et al. Quantitative Analysis of Computed Tomography Features of Different COVID-19 Infection Virus Variants Using Artificial Intelligence[J]. CT Theory and Applications, 2023, 32(5): 595-602. DOI: 10.15953/j.ctta.2023.043. (in Chinese).
Citation: ZHANG K, CHAI J, LIU R, et al. Quantitative Analysis of Computed Tomography Features of Different COVID-19 Infection Virus Variants Using Artificial Intelligence[J]. CT Theory and Applications, 2023, 32(5): 595-602. DOI: 10.15953/j.ctta.2023.043. (in Chinese).
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Quantitative Analysis of Computed Tomography Features of Different COVID-19 Infection Virus Variants Using Artificial Intelligence

  • Department of Radiology, Inner Mongolia People's Hospital, Huhhot 010017, China

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  • Received Date: March 09, 2023
  • Revised Date: March 20, 2023
  • Accepted Date: April 11, 2023
  • Available Online: April 26, 2023
  • Published Date: September 21, 2023
    Graphical Abstract
    • Abstract
  • Objective: To quantitatively analyze and compare the chest computed tomography (CT) imaging features of patients infected with delta and omicron variants of COVID-19 using artificial intelligence (AI). Method: The clinical data of 294 patients infected with the novel coronavirus delta variant diagnosed at the Fourth Hospital of Inner Mongolia Autonomous Region from February 20, 2022 to April 19, 2022 and 222 patients infected with the omicron variant diagnosed at the People's Hospital of Inner Mongolia Autonomous Region from December 1, 2022 to December 30, 2022 were retrospectively analyzed. CT imaging data were analyzed and divided into delta and omicron groups. Quantitative calculation was performed using deductive predictive pulmonary infection auxiliary diagnostic software, and CT imaging signs and quantitative CT data between groups were compared and analyzed. Results: No statistical significance was noted between the delta and omicron groups in imaging signs, such as ground-glass opacity, ground-glass nodule, cord-like lesion, consolidation, paving stone sign, thickened interlobular septum, and thickened vessels in the lesion. The distribution of lesions along the bronchial vascular bundle was more likely in the omicron than in the delta group. The total lung lesion volume, volume proportion, right middle lobe lesion volume, volume proportion, right inferior lobe lesion volume, and volume proportion in the delta group were higher than those in the omicron group. The proportions of lesions in the delta group in −570 ~ −470 HU, −470 ~ −370 HU, −370 ~ −270 HU, and −270 ~ −170 HU volumes were higher than those in the omicron group. Conclusion: In the early stage of COVID-19, the volume of CT lesions in the patients infected with the delta variant was higher than that in the omicron group, and the distribution of lesions in the omicron group was more likely to have atypical distribution along the bronchial vascular bundle than that in the delta group. The volume and volume proportion of the pneumonia-infected area in patients with COVID-19 were quantitatively evaluated using the AI-assisted diagnosis system for COVID-19 to provide objective reference data for patients' condition assessment.
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