Objective:To investigate the potential clinical practice of artificial intelligence(AI)-assisted diagnostic system(AI-ADS)for pneumonia in the post-pandemic era by exploring various ap-plication scenarios in different patient cohorts.Methods:The study retrospectively collected 1049 sets of chest CT scans from patients either diagnosed as normal(n=400),COVID-19(n=233),or other community-acquired pneumonia(CAP)(n=416)at three hospitals.We explored the potential clinical practice by validating its performance in the detection,classification,and lesion measurement(segmen-tation)of different types of pneumonia.Six senior radiologists participated in the establishment of the gold standard for lesion labeling and segmentation.Sensitivity,specificity,Dice coefficient,and area un-der the receiver operating characteristic curve(AUC)were utilized to evaluate the performance.Results:AI-ADS displayed decent detection performance on different types of pneumonia,as evidenced by the AUC of 0.968,983,0.992,0.941,and 0.958 for overall types,bacterial,COVID-19,non-COVID viral,and other pneumonia,respectively.The detection sensitivity all reached above 0.9.Additionally,the system differentiated viral and non-viral pneumonia with an AUC of 0.950,a sensitivity of 0.885,and a specificity of 0.910.Of note,AI-ADS achieved good segmentation results on both COVID-19 ca-ses(internal test set,averaged DICE=0.851)and non-COVID cases(external test set,averaged DICE=0.753).Conclusion:With performance improvement,Al-ADS can detect various types of pneumonia and differentiate viral pneumonia from others.It shows a decent lesion segmentation capacity among different types of pneumonia,indicating its potential clinical application in the post-pandemic era.
COVID-19Community-acquired pneumoniaTomography,X-ray computedAr-tificial intelligenceAssisted diagnostic system
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