ISSN 1004-4140
CN 11-3017/P
Volume 30 Issue 6
Nov.  2021
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LIU Li, CHEN Hong, ZHONG Wei, WEI Dongmei, SONG Yongli, LI Huixin, ZHOU Xin, GAO Xiaolong. Study on Predicting and Evaluating Clinical Classification of COVID-19 Pneumonia by Artificial Intelligence CT Quantitative Analysis[J]. CT Theory and Applications, 2021, 30(6): 743-751. DOI: 10.15953/j.1004-4140.2021.30.06.10
Citation: LIU Li, CHEN Hong, ZHONG Wei, WEI Dongmei, SONG Yongli, LI Huixin, ZHOU Xin, GAO Xiaolong. Study on Predicting and Evaluating Clinical Classification of COVID-19 Pneumonia by Artificial Intelligence CT Quantitative Analysis[J]. CT Theory and Applications, 2021, 30(6): 743-751. DOI: 10.15953/j.1004-4140.2021.30.06.10
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Study on Predicting and Evaluating Clinical Classification of COVID-19 Pneumonia by Artificial Intelligence CT Quantitative Analysis

  • 1. Clinical Research Team, The First Hospital of Qiqihar, Qiqihar 161005, China;

  • 2. Hangzhou Yitu Medical Technology Co, Ltd. Hangzhou 310024, China;

  • 3. School of Medicine and Technology, Qiqihar Medical University, Qiqihar 161000, China

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  • Received Date: June 20, 2021
  • Available Online: November 03, 2021
    Graphical Abstract
    • Abstract
  • Objective: To evaluate the correlation of CT artificial intelligence quantitative analysis in prediction and evaluation of clinical classification of COVID-19 pneumonia. Methods: The clinical and CT imaging data of 46 patients with COVID-19 treated in the hospital from February 1st, 2019 to January 20th, 2021 was retrospectively analyzed. We compared the correlation between the total lung infection volume, grinding glass density volume (GGO volume), solid density volume (SO volume) and clinical classification when artificial intelligence (AI) quantitative analysis was applied. Results: Among the 26 cases of common type, 16 cases of severe type and 4 cases of critical type, the main clinical manifestations were fever, cough and fatigue. Severe and critical types were more common in elder patients. The CT manifestations of three clinical types of pulmonary lesions were mainly GGO; total lung infection volume, GGO volume, SO volume in common type were smaller than that in patients of severe/critical type. Spearman grade correlation analysis showed significant correlation between total lung infection volume, GGO volume, and SO volume with clinical classification (0.86, 0.87, 0.84). Conclusion: The artificial intelligence CT quantitative index analysis (infection volume, GGO volume, SO volume) holds much correlation with clinical classification of COVID-19 pneumonia.
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