AI-based analysis of lung CT imaging changes in severe COVID-19 patients during recovery
YUAN Mengqi
DONG Jinghui
ZHANG Ziying
PAN Yuefei
ZHANG Yujie
HUANG Xin
LI Yuanyuan
HUANG Lei
XU Zhe
LI Yonggang
WANG Fusheng
SHI Lei
Abstract：Objective The Coronavirus Disease 2019(COVID-19)has significantly impacted global health,particularly as some recovering patients exhibit symptoms of Long-COVID and pulmonary imaging abnormalities.However,the long-term dynamic characteristics of lung CT imaging in these patients remain insufficiently understood.This study employs artificial intelligence(AI)quantitative techniques to dynamically analyze lung CT changes over the course of one year in patients with severe COVID-19,providing insights for the management and treatment of Long-COVID.Methods A total of 58 subjects from the placebo arm of a stem cell therapy COVID-19 cohort were included.Clinical and imaging data were collected at 6 time points:baseline,and at 1,3,6,9,and 12 months.AI-driven quantitative techniques were used to assess lesion mass,volume,and density,and to analyze the location,extent,and component ratios of lesions,depicting the radiographic evolution of lung changes in severe COVID-19 patients over one year.Results Most patients exhibited bilateral lung involvement,with a few showing unilateral involvement.The extent of lung involvement varied across lobes,with the right lower lobe being the most severely affected,having the largest infection volume and volume ratio,and the fewest patients recovering over time.Based on CT density values in Hounsfield units,ground-glass opacities were more frequently observed,while consolidation volumes constituted a smaller proportion of the total lung volume.Over time,the infected regions demonstrated a general trend of decreasing density,volume,and volume ratio,with a gradual decline from 0 to 6 months,followed by a slight increase after 6 months.At 12 months,residual lung abnormalities were still present in all patients'CT scans.Conclusions This study,utilizing AI-based quantitative techniques,revealed that lung lesions in severe COVID-19 patients gradually resolved during the recovery period.However,abnormal lung imaging findings persisted in all patients after one year.Prolonged follow-up studies are necessary to monitor the progression and characteristics of lung lesions in COVID-19 patients,as this is crucial for understanding long-term prognosis and mechanisms related to Long-COVID.
Keywords：COVID-19Long-COVID syndromelung CTartificial intelligencelong-term follow-up
Publication Date：2024-10-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 385-393 )