Artificial intelligence efficiently predicts gastric lesions,Helicobacter pylori infection and lymph node metastasis upon endoscopic images  

作　　者：Ruixin Yang Jialin Zhang Fengsheng Zhan Chao Yan Sheng Lu Zhenggang Zhu Kang An Jing Sun Yingyan Yu 

机构地区：[1]Department of General Surgery of Ruijin Hospital,Shanghai Institute of Digestive Surgery,and Shanghai Key Laboratory for Gastric Neoplasms,Shanghai Jiao Tong University School of Medicine,Shanghai 200025,China [2]Department of Gastroenterology of Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,Gubei Branch,Shanghai 201103,China [3]College of Information,Mechanical and Electrical Engineering,Shanghai Normal University,Shanghai 201418,China [4]Department of Gastroenterology of Ruijin Hospital,Shanghai Jiao Tong University School of Medicine,Shanghai 200025,China

出　　处：《Chinese Journal of Cancer Research》2024年第5期489-502,共14页中国癌症研究（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.82473013,82072602,82270575 and 82070558);the Shanghai Science and Technology Committee(No.20DZ2201900);the Innovation Foundation of Translational Medicine of Shanghai Jiao Tong University School of Medicine(No.TM202001);the Collaborative Innovation Center for Clinical and Translational Science by Chinese Ministry of Education&Shanghai Municipal Government(No.CCTS-2022202 and CCTS-202302)。

摘　　要：Objective:Medical images have been increased rapidly in digital medicine era,presenting an opportunity for the intervention of artificial intelligence(AI).In order to explore the value of convolutional neural network(CNN)algorithms in endoscopic images,we developed an AI-assisted comprehensive analysis system for endoscopic images and explored its performance in clinical real scenarios.Methods:A total of 6,270 white light endoscopic images from 516 cases were used to train 14 different CNN models.The images were divided into training set,validation set and test set according to 7:1:2 for exploring the possibility of discrimination of gastric cancer(GC)and benign lesions(nGC),gastric ulcer(GU)and ulcerated cancer(UCa),early gastric cancer(EGC)and nGC,infection of Helicobacter pylori(Hp)and no infection of Hp(noHp),as well as metastasis and no-metastasis at perigastric lymph nodes.Results:Among the 14 CNN models,EfficientNetB7 revealed the best performance on two-category of GC and nGC[accuracy:96.40%and area under the curve(AUC)=0.9959],GU and UCa(accuracy:90.84%and AUC=0.8155),EGC and nGC(accuracy:97.88%and AUC=0.9943),and Hp and noHp(accuracy:83.33%and AUC=0.9096).Whereas,InceptionV3 model showed better performance on predicting metastasis and nometastasis of perigastric lymph nodes for EGC(accuracy:79.44%and AUC=0.7181).In addition,the integrated analysis of endoscopic images and gross images of gastrectomy specimens was performed on 95 cases by EfficientNetB7 and RFB-SSD object detection model,resulting in 100%of predictive accuracy in EGC.Conclusions:Taken together,this study integrated image sources from endoscopic examination and gastrectomy of gastric tumors and incorporated the advantages of different CNN models.The AI-assisted diagnostic system will play an important role in the therapeutic decision-making of EGC.

关 键 词：Endoscopic images CNN early gastric cancer gastric ulcer artificial intelligence 

分 类 号：R735.2[医药卫生—肿瘤]