Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

查看全文 作　　者：Sheng-Bing [1]Zhao;Wei [2]Yang;Shu-Ling [3]Wang;Peng [3]Pan;Run-Dong [3]Wang;Xin [3]Chang;Zhong-Qian [2]Sun;Xing-Hui [2]Fu;Hong [2]Shang;Jian-Rong [4]Wu;Li-Zhu [1]Chen;Jia [1]Chang;Pu [1]Song;Ying-Lei [5]Miao;Shui-Xiang [6]He;Lin [7]Miao;Hui-Qing [8]Jiang;Wen [9]Wang;Xia [10]Yang;Yuan-Hang [3]Dong;Han [3]Lin;Yan [3]Chen;Jie [3]Gao;Qian-Qian [3]Meng;Zhen-Dong [3]Jin;Zhao-Shen [3]Li;Yu [3]Bai

机构地区：[1]Changhai Hospital,Second Military Medical University/Naval Medical University,Shanghai 200433,China;[2]Tencent AI Lab,National Open Innovation Platform for Next Generation Artificial Intelligence on Medical Imaging,Shenzhen 518063,Guangdong Province,China;[3]Department of Gastroenterology,Changhai Hospital,Second Military Medical University/Naval Medical University,Shanghai 200433,China;[4]Tencent Healthcare(Shenzhen)Co.LTD.,Shenzhen 518063,Guangdong Province,China;[5]Department of Gastroenterology,The First Affiliated Hospital of Kunming Medical University,Kunming 650000,Yunnan Province,China;[6]Department of Gastroenterology,The First Affiliated Hospital of Xi'an Jiaotong University,Xi'an 710061,Shaanxi Province,China;[7]Institute of Digestive Endoscopy and Medical Center for Digestive Disease,The Second Affiliated Hospital of Nanjing Medical University,Nanjing 210011,Jiangsu Province,China;[8]Department of Gastroenterology,The Second Hospital of Hebei Medical University,Hebei Key Laboratory of Gastroenterology,Hebei Institute of Gastroenterology,Shijiazhuang 050000,Hebei Province,China;[9]Department of Gastroenterology,900th Hospital of Joint Logistics Support Force,Fuzhou 350025,Fujian Province,China;[10]Department of Gastroenterology,No.905 Hospital of The Chinese People's Liberation Army,Shanghai 200050,China

出　　处：《World Journal of Gastroenterology》索引2021年第27卷第31期,共15页

基　　金：the National Key R&D Program of China,No.2018YFC1313103;the National Natural Science Foundation of China,No.81670473 and No.81873546;the“Shu Guang”Project of Shanghai Municipal Education Commission and Shanghai Education Development Foundation,No.19SG30;the Key Area Research and Development Program of Guangdong Province,China,No.2018B010111001. 摘　　要：BACKGROUND Artificial intelligence in colonoscopy is an emerging field,and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas.Several deep learning-based computer-assisted detection(CADe)techniques were established from small single-center datasets,and unrepresentative learning materials might confine their application and generalization in wide practice.Although CADes have been reported to identify polyps in colonoscopic images and videos in real time,their diagnostic performance deserves to be further validated in clinical practice.AIM To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.METHODS With high-quality screening and labeling from 55 qualified colonoscopists,a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe.In addition,the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps.Finally,we conducted a selfcontrolled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.RESULTS The CADe was able to identify polyps in the test dataset with 95.0%sensitivity and 99.1%specificity.For colonoscopy videos,all 86 polyps were detected with 92.2%sensitivity and 93.6%specificity in frame-by-frame analysis.In the prospective validation,the sensitivity of CAD in identifying polyps was 98.4%(185/188).Folds,reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies.Colonoscopists can detect more polyps(0.90 vs 0.82,P<0.001)and adenomas(0.32 vs 0.30,P=0.045)with the aid of CADe,particularly polyps<5 mm and flat polyps(0.65 vs 0.57,P<0.001;0.74 vs 0.67,P=0.001,respectively).However,high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time(P=0.32;P=0.16,respectively).CONCLUSION CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas,and further confirmation is warranted. 关键词：Computer-assisted detection Artificial intelligence Deep learning COLONOSCOPY Clinical validation Colorectal polyp

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Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

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