Endoscopy 2021; 53(12): 1199-1207
DOI: 10.1055/a-1350-5583
Original article

Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial

Lianlian Wu‡   *
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Xinqi He‡   *
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Mei Liu
 4   Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Huaping Xie
 4   Department of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Ping An
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Jun Zhang
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Heng Zhang
 5   Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Yaowei Ai
 6   Department of Gastroenterology, The People’s Hospital of China Three Gorges University/The First People’s Hospital of Yichang, Yichang, China
,
Qiaoyun Tong
 7   Department of Gastroenterology, Yichang Central People’s Hospital, China Three Gorges University, Yichang, China
,
Mingwen Guo
 6   Department of Gastroenterology, The People’s Hospital of China Three Gorges University/The First People’s Hospital of Yichang, Yichang, China
,
Manling Huang
 5   Department of Gastroenterology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Cunjin Ge
 7   Department of Gastroenterology, Yichang Central People’s Hospital, China Three Gorges University, Yichang, China
,
Zhi Yang
 7   Department of Gastroenterology, Yichang Central People’s Hospital, China Three Gorges University, Yichang, China
,
Jingping Yuan
 8   Department of Pathology, Renmin Hospital of Wuhan University, Wuhan, China
,
Jun Liu
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Wei Zhou
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Xiaoda Jiang
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Xu Huang
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Ganggang Mu
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Xinyue Wan
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Yanxia Li
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Hongguang Wang
 9   Department of Gastroenterology, Jilin People’s Hospital, Jilin, China
,
Yonggui Wang
10   School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
,
Hongfeng Zhang
11   Department of Pathology, Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
,
Di Chen
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Dexin Gong
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Jing Wang
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Li Huang
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Jia Li
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Liwen Yao
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Yijie Zhu
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
,
Honggang Yu
 1   Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
 2   Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China
 3   Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Renmin Hospital of Wuhan University, Wuhan, China
› Author AffiliationsSupported by: Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision 2018BCC337
Supported by: Hubei Province Major Science and Technology Innovation Project 2018–916–000–008

Trial Registration: Chinese Clinical Trial Registry Registration number (trial ID): ChiCTR1800018403 Type of study: Randomized, Multi-Center Study
› Further Information
Also available at
   Permissions and Reprints

Referred to by:

Author commentary on Lianlian Wu et al.Endoscopy 2021; 53(12): v57-v57
DOI: 10.1055/a-1391-1567

Referred to by:

Artificial intelligence for gastric cancer: can we make further progress?Endoscopy 2021; 53(12): 1208-1209
DOI: 10.1055/a-1471-3474

Abstract

Background Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial.

Methods ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting.

Results 1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer.

Conclusions In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

* These authors contribute equally to this work.


Supplementary material



Publication History

Received: 21 September 2020

Accepted after revision: 11 January 2021

Accepted Manuscript online:
11 January 2021

Article published online:
04 March 2021

© 2021. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Bisschops R, Areia M, Coron E. et al. Performance measures for upper gastrointestinal endoscopy: a European Society of Gastrointestinal Endoscopy (ESGE) Quality Improvement Initiative. Endoscopy 2016; 48: 843-864
    Thieme ConnectPubMedSearch in Google Scholar
  • 2 Pasechnikov V, Chukov S, Fedorov E. et al. Gastric cancer: prevention, screening and early diagnosis. World J Gastroenterol 2014; 20: 13842-13862
    CrossrefPubMedSearch in Google Scholar
  • 3 Kaise M. Advanced endoscopic imaging for early gastric cancer. Best Pract Res Clin Gastroenterol 2015; 29: 575-587
    CrossrefPubMedSearch in Google Scholar
  • 4 Axon A. Is diagnostic and therapeutic endoscopy currently appropriate?. suggestions for improvement. Best Pract Res Clin Gastroenterol 2008; 22: 959-970
    CrossrefPubMedSearch in Google Scholar
  • 5 Gado AS, Ebeid BA, Axon AT. Quality assurance in gastrointestinal endoscopy: an Egyptian experience. Arab J Gastroenterol 2016; 17: 153-158
    CrossrefPubMedSearch in Google Scholar
  • 6 Malheiro R, de Monteiro-Soares M, Hassan C. et al. Methodological quality of guidelines in gastroenterology. Endoscopy 2014; 46: 513-525
    Thieme ConnectPubMedSearch in Google Scholar
  • 7 Rizk MK, Sawhney MS, Cohen J. et al. Quality indicators common to all GI endoscopic procedures. Gastrointest Endosc 2015; 81: 3-16
    CrossrefPubMedSearch in Google Scholar
  • 8 Teh JL, Tan JR, Lau LJ. et al. Longer examination time improves detection of gastric cancer during diagnostic upper gastrointestinal endoscopy. Clin Gastroenterol Hepatol 2015; 13: 480-487
    CrossrefPubMedSearch in Google Scholar
  • 9 Ito Y, Blackstone MO. The endoscopic diagnosis of early gastric cancer. Gastrointest Endosc 1979; 25: 96-101
    CrossrefPubMedSearch in Google Scholar
  • 10 Yao K. The endoscopic diagnosis of early gastric cancer. Ann Gastroenterol 2013; 26: 11-22
    PubMedSearch in Google Scholar
  • 11 Rutter MD, Rees CJ. Quality in gastrointestinal endoscopy. Endoscopy 2014; 46: 526-528
    Thieme ConnectPubMedSearch in Google Scholar
  • 12 Litjens G, Kooi T, Bejnordi BE. et al. A survey on deep learning in medical image analysis. Med Image Anal 2017; 42: 60-88
    CrossrefPubMedSearch in Google Scholar
  • 13 Urban G, Tripathi P, Alkayali T. et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology 2018; 155: 1069-1078
    CrossrefPubMedSearch in Google Scholar
  • 14 Hirasawa T, Aoyama K, Tanimoto T. et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21: 653-660
    CrossrefPubMedSearch in Google Scholar
  • 15 Wu L, Zhou W, Wan X. et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy 2019; 51: 522-531
    Thieme ConnectPubMedSearch in Google Scholar
  • 16 Wu L, Zhang J, Zhou W. et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut 2019; 68: 2161-2169
    CrossrefPubMedSearch in Google Scholar
  • 17 Shao L, Zhu F, Li X. Transfer learning for visual categorization: a survey. IEEE Trans Neural Netw Learn Syst 2015; 26: 1019-1034
    CrossrefPubMedSearch in Google Scholar
  • 18 Leung WK, Wu M-s, Kakugawa Y. et al. Screening for gastric cancer in Asia: current evidence and practice. Lancet Oncol 2008; 9: 279-287
    CrossrefPubMedSearch in Google Scholar
  • 19 Karimi P, Islami F, Anandasabapathy S. et al. Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiol Biomarkers Prev 2014; 23: 700-713
    CrossrefPubMedSearch in Google Scholar
  • 20 Huang Q, Shi J, Sun Q. et al. Clinicopathological characterisation of small (2 cm or less) proximal and distal gastric carcinomas in a Chinese population. Pathology 2015; 47: 526-532
    CrossrefPubMedSearch in Google Scholar
  • 21 Jin EH, Lee D, Bae JH. et al. Improved accuracy in optical diagnosis of colorectal polyps using convolutional neural networks with visual explanations. Gastroenterology 2020; 158: 2169-2179
    CrossrefPubMedSearch in Google Scholar
  • 22 Li L, Chen Y, Shen Z. et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer 2020; 23: 126-132
    CrossrefPubMedSearch in Google Scholar
  • 23 Tajbakhsh N, Gurudu SR, Liang J. Automatic polyp detection in colonoscopy videos using an ensemble of convolutional neural networks. Proceedings of the 12th International Symposium on Biomedical Imaging (ISBI); 2015 April 16–19; Brooklyn, NY, USA. IEEE 2015; July; 79–83.
  • 24 Hazirbas C, Ma L, Domokos C. et al. Fusenet: incorporating depth into semantic segmentation via fusion-based cnn architecture. Asian conference on computer vision. Cham: Springer 2016: 213-228
    PubMed
  • 25 Dinis-Ribeiro M, Areia M, de Vries AC. et al. Management of precancerous conditions and lesions in the stomach (MAPS): guideline from the European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter Study Group (EHSG), European Society of Pathology (ESP), and the Sociedade Portuguesa de Endoscopia Digestiva (SPED). Endoscopy 2012; 44: 74-94
    Thieme ConnectPubMedSearch in Google Scholar
  • 26 Widya AR, Monno Y, Okutomi M. et al. Whole stomach 3D reconstruction and frame localization from monocular endoscope video. IEEE J Transl Eng Health Med 2019; 7: 1-10
    CrossrefPubMedSearch in Google Scholar
  • 27 He K, Zhang X, Ren S. et al. Deep residual learning for image recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 June 27–30; Las Vegas, NV, USA. IEEE 2016: 770–778.
  • 28 Du W, Rao N, Liu D. et al. Review on the applications of deep learning in the analysis of gastrointestinal endoscopy images. IEEE Access 2019; 7: 142053-142069
    CrossrefPubMedSearch in Google Scholar
  • 29 Liu X, Wang C, Bai J. et al. Fine-tuning pre-trained convolutional neural networks for gastric precancerous disease classification on magnification narrow-band imaging images. Neurocomputing 2020; 392: 253-267
    CrossrefPubMedSearch in Google Scholar
  • 30 Zhou Z. Machine learning (in Chinese) [M]. Beijing, China: Tsinghua University Press; 2016
    Search in Google Scholar
  • 31 Lequan Y, Hao C, Qi D. et al. Integrating online and offline three-dimensional deep learning for automated polyp detection in colonoscopy videos. IEEE J Biomed Health Inform 2017; 21: 65-75
    CrossrefPubMedSearch in Google Scholar
  • 32 An P, Yang D, Wang J. et al. A deep learning method for delineating early gastric cancer resection margin under chromoendoscopy and white light endoscopy. Gastric Cancer 2020; 23: 884-892
    CrossrefPubMedSearch in Google Scholar