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
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations
Deep‑learning reconstruction (DLR) shifts CT image formation from a hardware‑limited process to a data‑driven one. In our real‑world cohort of >10,000 body scans, we observed a
28. April 2025
