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Related Experiment Video

Updated: Nov 11, 2025

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Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach.

Lu Huang1, Rui Han1, Tao Ai1

  • 1Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue 1095, 430030 Wuhan, China (L.H., T.A., L.X.); Department of Radiology, Wuhan No. 1 Hospital, Wuhan, China (R.H.); Institute of Advanced Research, Infervison, Beijing, China (P.Y., H.K.); and Division of Imaging Processing, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands (Q.T.).

Radiology. Cardiothoracic Imaging
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Summary

Deep learning analysis of serial CT scans quantitatively assessed lung burden changes in coronavirus disease 2019 (COVID-19) patients. This automated method showed significant differences in lung opacification across clinical severity groups.

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Pulmonology

Background:

  • Coronavirus disease 2019 (COVID-19) presents with diverse pulmonary manifestations.
  • Accurate quantitative assessment of lung involvement is crucial for patient management and prognosis.
  • Traditional methods for evaluating lung opacification can be subjective.

Purpose of the Study:

  • To quantitatively evaluate lung burden changes in COVID-19 patients using serial CT scans.
  • To employ an automated deep learning method for precise quantification of lung opacification.
  • To compare quantitative lung burden changes across different clinical severity types of COVID-19.

Main Methods:

  • Retrospective evaluation of 126 COVID-19 patients who underwent chest CT scans.
  • Classification of patients into mild, moderate, severe, and critical groups based on clinical and CT findings.
  • Automated quantification of whole-lung and lobar opacification percentages using commercial deep learning software.
  • Comparison of opacification percentages at baseline, first, and second follow-up CT scans.
  • Longitudinal analysis of quantitative CT parameters across clinical severity groups.

Main Results:

  • CT-derived opacification percentage significantly differed among clinical groups at baseline, increasing with severity (P < .01).
  • Whole-lung opacification significantly increased from baseline to first follow-up CT (median: 3.6% vs 8.7%, P < .01).
  • No significant progression of opacification was observed between the first and second follow-up CT scans (P = .655).

Conclusions:

  • Deep learning-based quantification of lung opacification in COVID-19 is significantly associated with clinical severity.
  • This automated approach offers objective initial assessment and follow-up of pulmonary findings in COVID-19.
  • The method has the potential to reduce subjectivity in evaluating COVID-19 related lung changes.