ARTIFICAL INTELLIGENCE (AI) IN UPPER GASTROINTESTINAL ENDOSCOPY

Üsküdar Berkay Çaralan

Ankara Bilkent City Hospital, Department of Gastrointestinal Surgery, Ankara, Türkiye

Çaralan ÜB. Artifical Intelligence (AI) in Upper Gastrointestinal Endoscopy. Çaycı HM, ed. Artificial Intelligence (AI) in Gastrointestinal Surgery. 1st ed. Ankara: Türkiye Klinikleri; 2025. p.165-172.

ABSTRACT

Artificial intelligence (AI) is used in upper gastrointestinal endoscopy to identify premalignant/malignant lesions related to the esophagus and stomach, as well as to evaluate conditions such as tumor invasion. These systems are expected to show high accuracy and to obtain better results with future developments. In addition to these, more accurate and faster results will be obtained with systems that will assist endoscopists at the time of diagnosis.

Keywords: Artificial intelligence; Upper gastrointestinal endoscopy; Esophageal cancer; Gastric cancer

Referanslar

  1. Rey JF, Lambert R ESGE Quality Assurance Committee. ESGE recommendations for quality control in gastrointestinal endoscopy: guidelines for image documentation in upper and lower GI endoscopy. Endoscopy. 2001;33:901-903. [Crossref]  [PubMed]
  2. Kaul V, Enslin S, Gross SA. History of artificial intelligence in medicine. Gastrointest. Endosc. 2020;92:807-812. [Crossref]  [PubMed]
  3. Mori Y, Kudo S, Mohmed HEN, Misawa M, Ogata N, Itoh H, et al. Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective. Dig. Endosc. 2019;31:378-388. [Crossref]  [PubMed]
  4. Huang L-M, Yang W-J, Huang Z-Y, et al. Artificial intelligence technique in detection of early esophageal cancer. World J Gastroenterol. 2020;26:5959-5969. [Crossref]  [PubMed]  [PMC]
  5. Ebigbo A, Palm C, Probst A, Mendel R, Manzeneder J, Prinz F, et al. A technical review of artificial intelligence as applied to gastrointestinal endoscopy: Clarifying the terminology. Endosc. Int. Open. 2019;7:E1616-E1623. [Crossref]  [PubMed]  [PMC]
  6. Mori Y, Kudo S, Mohmed HEN, Misawa M, Ogata N, Itoh H, et al. Artificial intelligence and upper gastrointestinal endoscopy: Current status and future perspective. Dig. Endosc. 2019;31:378-388. [Crossref]  [PubMed]
  7. Parasa S, Wallace M, Bagci U, et al. Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit. Gastrointest Endosc. 2020;92(4):938-945.e1. [Crossref]  [PubMed]
  8. Bujanda DE, Hachem C. Barrett's Esophagus. Mo Med. 2018 May-Jun;115(3):211-213. [PMC]
  9. Weusten B, Bisschops R, Coron E, Dinis-Ribeiro M, Dumonceau JM, Esteban JM, et al. Endoscopic management of Barrett's esophagus: European Society of Gastrointestinal Endoscopy (ESGE) Position Statement. Endoscopy. 2017 Feb;49(2):191-198. [Crossref]  [PubMed]
  10. Kusano C, Singh R, Lee YY, Soh YSA, Sharma P, Ho K, et al. Global variations in diagnostic guidelines for Barrett's esophagus. Dig. Endosc. 2022;34:1320-1328. [Crossref]  [PubMed]
  11. Smyth EC, Lagergren J, Fitzgerald RC, Lordick F, Shah MA, Lagergren P, Cunningham D. Oesophageal cancer. Nat. Rev. Dis. Primers. 2017;3:17048. [Crossref]  [PubMed]  [PMC]
  12. van der Sommen F, Zinger S, Curvers WL, et al. Computer-aided detection of early neoplastic lesions in Barrett's esophagus. Endoscopy 2016;48:617-24. [Crossref]  [PubMed]
  13. Struyvenberg MR, de Groof AJ, van der Putten J, van der Sommen F, Baldaque-Silva F, Omae M, et al. A computer-assisted algorithm for narrow-band imaging-based tissue characterization in Barrett's esophagus. Gastrointest. Endosc. 2021;93:89-98. [Crossref]  [PubMed]
  14. Swager AF, van der Sommen F, Klomp SR, Zinger S, Meijer SL, Schoon EJ, et al. Computer-aided detection of early Barrett's neoplasia using volumetric laser endomicroscopy. Gastrointest. Endosc. 2017;86:839-846. [Crossref]  [PubMed]
  15. Hashimoto R, Requa J, Dao T, et al. Artificial intelligence using convolutional neural networks for real-time detection of early esophageal neoplasia in Barrett's esophagus (with video). Gastrointest Endosc 2020;91:1264-1271.e1. [Crossref]  [PubMed]
  16. de Groof AJ, Struyvenberg MR, van der Putten J, et al. Deep-learning system detects neoplasia in patients with Barrett's esophagus with higher accuracy than endoscopists in a multistep training and validation study with benchmarking. Gastroenterology 2020; 158: 915-929.e4. [Crossref]  [PubMed]
  17. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer Journal for Clinicians, 2018;68(6):394-424. [Crossref]  [PubMed]
  18. Horie, Yoshimasa, et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointestinal endoscopy. 2010;89(1):25-32. [Crossref]  [PubMed]
  19. Feng Y, Liang Y, Li P, Long Q, Song J, Li M, et al. Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system. Discov. Oncol. 2023;14:73. [Crossref]  [PubMed]  [PMC]
  20. Shimamoto Y, Ishihara R, Kato Y. et al. Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence. J Gastroenterol. 2020;55:1037-1045. [Crossref]  [PubMed]
  21. Jin P, Ji X, Kang W, Li Y, Liu H, Ma F, et al. Artificial intelligence in gastric cancer: A systematic review. J. Cancer Res. Clin. Oncol. 2020;146:2339-2350. [Crossref]  [PubMed]
  22. Young E, Philpott H, Singh R. Endoscopic diagnosis and treatment of gastric dysplasia and early cancer: Current evidence and what the future may hold. World J. Gastroenterol. 2021;27:5126-5151. [Crossref]  [PubMed]  [PMC]
  23. Miyaki R, Yoshida S, Tanaka S, Kominami Y, Sanomura Y, Matsuo T, et al. Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. J. Gastroenterol. Hepatol. 2013;28:841-847. [Crossref]  [PubMed]
  24. Luo H, Xu G, Li C, He L, Luo L, Wang Z, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: A multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20:1645-1654. [Crossref]  [PubMed]
  25. Kubota K, Kuroda J, Yoshida M, Ohta K, Kitajima M. Medical image analysis: Computer-aided diagnosis of gastric cancer invasion on endoscopic images. Surg. Endosc. 2021;26:1485-1489. [Crossref]  [PubMed]
  26. Niikura R, Aoki T, Shichijo S, Yamada A, Kawahara T, Kato Y, et al. Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy. Endoscopy. 2022;54:780-784. [Crossref]  [PubMed]  [PMC]
  27. Kanesaka T, Lee TC, Uedo N, et al. Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. Gastrointest. Endosc. 2018;87:1339-44. [Crossref]  [PubMed]
  28. 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;21:653-60. [Crossref]  [PubMed]
  29. ACG clinical guideline for the diagnosis and management of gastroesophageal reflux disease. Katz PO, Dunbar KB, Schnoll-Sussman FH, Greer KB, Yadlapati R, Spechler SJ. Am J Gastroenterol. 2022;117:27-56. [Crossref]  [PubMed]  [PMC]
  30. Wang CC, Chiu YC, Chen WL, Yang TW, Tsai MC, Tseng, M.-H. A Deep Learning Model for Classification of Endoscopic Gastroesophageal Reflux Disease. Int. J. Environ. Res. Public Health. 2021;18(5):2428. [Crossref]  [PubMed]  [PMC]
  31. Wang CC, Chiu YC, Chen WL, Yang TW, Tsai MC, Tseng MH. A deep learning model for classification of endoscopic gastroesophageal reflux disease. Int J Environ Res Public Health. 2021;18:2428. [Crossref]  [PubMed]  [PMC]
  32. Ge Z, Wang B, Chang J, Yu Z, Zhou Z, Zhang J, et al. Using deep learning to assess the function of gastroesophageal flap valve according to the Hill classification system. Scand J Gastroenterol. 2023;58:596-604. [PMC]
  33. Bang CS, Lee JJ, Baik GH. Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis of Diagnostic Test Accuracy. J. Med. Internet Res. 2020;22:e21983. [Crossref]  [PubMed]  [PMC]
  34. Shichijo S, Nomura S, Aoyama K, Nishikawa Y, Miura M, Shinagawa T, et al. Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images. EBioMedicine. 2017;25:106-111. [Crossref]  [PubMed]  [PMC]
  35. Zheng W, Zhang X, Kim JJ, Zhu X, Ye G, Ye B, et al. High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience. Clin. Transl. Gastroenterol. 2019;10:e00109. [Crossref]  [PubMed]  [PMC]
  36. Hirotaka N, Hiroshi K, Hiroshi K, Nobuhiro S. Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: A single-center prospective study. Ann. Gastroenterol. 2018;31:462-468. [PMC]
  37. Data Science Central: Challenges to successful AI implementation in healthcare. Data Science Central. 30 Oct 2022. [Link]