AI deep learning for accurate recognition of different pathological types of early lung adenocarcinoma

CHEN Tangxinxi; XIAO Yunping; PAN Yongjun

Abstract：Objective To investigate the diagnostic efficacy of AI deep learning in different pathological types of early lung adenocarcinoma,and to analyze the application value of AI deep learning in different pathological types.Methods CT im-ages of 90 patients with early lung adenocarcinoma confirmed by surgery and pathology were selected and divided into in situ adenocarcinoma group,microinvasive adenocarcinoma group and invasive adenocarcinoma group,with 30 cases in each group,and were randomly divided into training group(n=60 cases)and verification group(n=30 cases)according to the ratio of 2:1.The joint film artificial intelligence research platform was used to semi-automatically segment the target lesions and ex-tract the image omics features,and the data of the validation group wene used to test the training deep learning model made.We analyzed the CT morphological features of the lesions,calculated the quantitative parameters of the lesions,and made a prediction score for the pathological types of lung adenocarcinoma so as to analyze the diagnostic effectiveness.Results There were no significant differences in gender,age,nodule location,nodule size,nodule volume,nodule mass,minimum CT value and cavitation sign among the three groups(P>0.05).There were statistically significant differences in nodule den-sity,long diameter,short diameter,volume,AI prediction score,maximum CT value,average CT value,lobular sign,vas-cular bunching sign,burr sign and pleural depression sign(P<0.05).The long diameter,short diameter and diameter of in-vasive adenocarcinoma were higher than those of in situ adenocarcinoma group and microinvasive adenocarcinoma group.The predicted AI score,maximum CT value and average CT value of invasive adenocarcinoma group were higher than those of in situ adenocarcinoma group and microinvasive adenocarcinoma group.The proportion of lobular sign,vascular bunching sign,burr sign and pleural sag sign in invasive adenocarcinoma group was higher than that in situ adenocarcinoma group and micro-invasive adenocarcinoma group.Conclusion AI learning based on artificial intelligence has high diagnostic value in the diag-nosis of different pathological types of lung adenocarcinoma.It can accurately identify different pathological types and has high repeatability and operability.