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In this retrospective study, the authors aimed to develop a fully automated artificial intelligence (AI) system to quantitatively assess the severity and progression of COVID-19 using thick-section chest CT images. Through their research and work, they were able to determine that a deep learning-based AI system built on thick-section CT imaging can accurately quantify COVID-19-associated abnormalities in the lung and assess the severity and progression of the disease.

Key points

  • A deep learning–based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74).
  • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97).
  • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen’s kappa 0.8220).

Article: From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans

Authors: Zhang Li, Zheng Zhong, Yang Li, Tianyu Zhang, Liangxin Gao, Dakai Jin, Yue Sun, Xianghua Ye, Li Yu, Zheyu Hu, Jing Xiao, Lingyun Huang & Yuling Tang

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