DOI QR코드

DOI QR Code

Role of artificial intelligence in diagnosing Barrett's esophagus-related neoplasia

  • Michael Meinikheim (Department of Gastroenterology, University Hospital of Augsburg) ;
  • Helmut Messmann (Department of Gastroenterology, University Hospital of Augsburg) ;
  • Alanna Ebigbo (Department of Gastroenterology, University Hospital of Augsburg)
  • Received : 2022.09.05
  • Accepted : 2022.11.25
  • Published : 2023.01.30

Abstract

Barrett's esophagus is associated with an increased risk of adenocarcinoma. Thorough screening during endoscopic surveillance is crucial to improve patient prognosis. Detecting and characterizing dysplastic or neoplastic Barrett's esophagus during routine endoscopy are challenging, even for expert endoscopists. Artificial intelligence-based clinical decision support systems have been developed to provide additional assistance to physicians performing diagnostic and therapeutic gastrointestinal endoscopy. In this article, we review the current role of artificial intelligence in the management of Barrett's esophagus and elaborate on potential artificial intelligence in the future.

Keywords

References

  1. Spechler SJ, Souza RF. Barrett's esophagus. N Engl J Med 2014;371:836-845. 
  2. Qumseya BJ, Bukannan A, Gendy S, et al. Systematic review and meta-analysis of prevalence and risk factors for Barrett's esophagus. Gastrointest Endosc 2019;90:707-717. 
  3. Smyth EC, Lagergren J, Fitzgerald RC, et al. Oesophageal cancer. Nat Rev Dis Primers 2017;3:17048. 
  4. Coleman HG, Xie SH, Lagergren J. The epidemiology of esophageal adenocarcinoma. Gastroenterology 2018;154:390-405. 
  5. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209-249. 
  6. Kambhampati S, Tieu AH, Luber B, et al. Risk factors for progression of Barrett's esophagus to high grade dysplasia and esophageal adenocarcinoma. Sci Rep 2020;10:4899. 
  7. Chandrasekar VT, Hamade N, Desai M, et al. Significantly lower annual rates of neoplastic progression in short- compared to long-segment non-dysplastic Barrett's esophagus: a systematic review and meta-analysis. Endoscopy 2019;51:665-672. 
  8. Visrodia K, Singh S, Krishnamoorthi R, et al. Magnitude of missed esophageal adenocarcinoma after Barrett's esophagus diagnosis: a systematic review and meta-analysis. Gastroenterology 2016;150:599-607. 
  9. Singer ME, Odze RD. High rate of missed Barrett's esophagus when screening with forceps biopsies. Esophagus 2023;20:143-149. 
  10. Sharma P, Bergman JJ, Goda K, et al. Development and validation of a classification system to identify high-grade dysplasia and esophageal adenocarcinoma in Barrett's esophagus using narrow-band imaging. Gastroenterology 2016;150:591-598. 
  11. ASGE Technology Committee, Thosani N, Abu Dayyeh BK, et al. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE Preservation and Incorporation of Valuable Endoscopic Innovations thresholds for adopting real-time imaging-assisted endoscopic targeted biopsy during endoscopic surveillance of Barrett's esophagus. Gastrointest Endosc 2016;83:684-698. 
  12. Qumseya BJ, Wang H, Badie N, et al. Advanced imaging technologies increase detection of dysplasia and neoplasia in patients with Barrett's esophagus: a meta-analysis and systematic review. Clin Gastroenterol Hepatol 2013;11:1562-1570. 
  13. Tholoor S, Bhattacharyya R, Tsagkournis O, et al. Acetic acid chromoendoscopy in Barrett's esophagus surveillance is superior to the standardized random biopsy protocol: results from a large cohort study (with video). Gastrointest Endosc 2014;80:417-424. 
  14. Chedgy FJ, Subramaniam S, Kandiah K, et al. Acetic acid chromoendoscopy: improving neoplasia detection in Barrett's esophagus. World J Gastroenterol 2016;22:5753-5760. 
  15. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56. 
  16. van der Sommen F, de Groof J, Struyvenberg M, et al. Machine learning in GI endoscopy: practical guidance in how to interpret a novel field. Gut 2020;69:2035-2045. 
  17. Hsiao CH, Lin PC, Chung LA, et al. A deep learning-based precision and automatic kidney segmentation system using efficient feature pyramid networks in computed tomography images. Comput Methods Programs Biomed 2022;221:106854. 
  18. Zou KH, Warfield SK, Bharatha A, et al. Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 2004;11:178-189. 
  19. van der Sommen F, Zinger S, Curvers WL, et al. Computer-aided detection of early neoplastic lesions in Barrett's esophagus. Endoscopy 2016;48:617-624. 
  20. 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. 
  21. de Groof AJ, Struyvenberg MR, Fockens KN, et al. Deep learning algorithm detection of Barrett's neoplasia with high accuracy during live endoscopic procedures: a pilot study (with video). Gastrointest Endosc 2020;91:1242-1250. 
  22. 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. 
  23. Iwagami H, Ishihara R, Aoyama K, et al. Artificial intelligence for the detection of esophageal and esophagogastric junctional adenocarcinoma. J Gastroenterol Hepatol 2021;36:131-136. 
  24. Ghatwary N, Zolgharni M, Ye X. Early esophageal adenocarcinoma detection using deep learning methods. Int J Comput Assist Radiol Surg 2019;14:611-621. 
  25. Struyvenberg MR, de Groof AJ, van der Putten J, et al. A computer-assisted algorithm for narrow-band imaging-based tissue characterization in Barrett's esophagus. Gastrointest Endosc 2021;93:89-98. 
  26. Hussein M, Gonzalez-Bueno Puyal J, Lines D, et al. A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks. United European Gastroenterol J 2022;10:528-537. 
  27. Ebigbo A, Mendel R, Probst A, et al. Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma. Gut 2019;68:1143-1145. 
  28. Ebigbo A, Mendel R, Probst A, et al. Real-time use of artificial intelligence in the evaluation of cancer in Barrett's oesophagus. Gut 2020;69:615-616. 
  29. Ebigbo A, Mendel R, Probst A, et al. Multimodal imaging for detection and segmentation of Barrett's esophagus-related neoplasia using artificial intelligence. Endoscopy 2022;54:E587. 
  30. Ebigbo A, Mendel R, Ruckert T, et al. Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study. Endoscopy 2021;53:878-883. 
  31. Lui TK, Tsui VW, Leung WK. Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest Endosc 2020;92:821-830. 
  32. Elsbernd BL, Dunbar KB. Volumetric laser endomicroscopy in Barrett's esophagus. Tech Innov Gastrointest Endosc 2021;23:P69-P76. 
  33. Smith MS, Cash B, Konda V, et al. Volumetric laser endomicroscopy and its application to Barrett's esophagus: results from a 1,000 patient registry. Dis Esophagus 2019;32:doz029. 
  34. Trindade AJ, McKinley MJ, Fan C, et al. Endoscopic surveillance of Barrett's esophagus using volumetric laser endomicroscopy with artificial intelligence image enhancement. Gastroenterology 2019;157:303-305. 
  35. Struyvenberg MR, de Groof AJ, Fonolla R, et al. Prospective development and validation of a volumetric laser endomicroscopy computer algorithm for detection of Barrett's neoplasia. Gastrointest Endosc 2021;93:871-879. 
  36. Waterhouse DJ, Januszewicz W, Ali S, et al. Spectral endoscopy enhances contrast for neoplasia in surveillance of Barrett's esophagus. Cancer Res 2021;81:3415-3425. 
  37. Sharma P, Savides TJ, Canto MI, et al. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on imaging in Barrett's Esophagus. Gastrointest Endosc 2012;76:252-254. 
  38. Pan W, Li X, Wang W, et al. Identification of Barrett's esophagus in endoscopic images using deep learning. BMC Gastroenterol 2021;21:479. 
  39. Ali S, Bailey A, Ash S, et al. A pilot study on automatic three-dimensional quantification of Barrett's esophagus for risk stratification and therapy monitoring. Gastroenterology 2021;161:865-878. 
  40. Beg S, Ragunath K, Wyman A, et al. Quality standards in upper gastrointestinal endoscopy: a position statement of the British Society of Gastroenterology (BSG) and Association of Upper Gastrointestinal Surgeons of Great Britain and Ireland (AUGIS). Gut 2017;66:1886-1899. 
  41. 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. 
  42. 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.