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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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This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.