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Artificial Intelligence in Colonoscopy

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Abstract

Purpose of Review

Artificial intelligence (AI) is a rapidly growing field in gastrointestinal endoscopy, and its potential applications are virtually endless, with studies demonstrating use of AI for early gastric cancer, inflammatory bowel disease, Barrett’s esophagus, capsule endoscopy, as well as other areas in gastroenterology. Much of the early studies and applications of AI in gastroenterology have revolved around colonoscopy, particularly with regards to real-time polyp detection and characterization. This review will cover much of the existing data on computer-aided detection (CADe), computer-aided diagnosis (CADx), and briefly discuss some other interesting applications of AI for colonoscopy, while also considering some of the challenges and limitations that exist around the use of AI for colonoscopy.

Recent Findings

Multiple randomized controlled trials have now been published which show a statistically significant improvement when using AI to improve adenoma detection and reduce adenoma miss rates during colonoscopy. There is also a growing pool of literature showing that AI can be helpful for characterizing/diagnosing colorectal polyps in real time. AI has also shown promise in other areas of colonoscopy, including polyp sizing and automated measurement and monitoring of quality metrics during colonoscopy.

Summary

AI is a promising tool that has the ability to shape the future of gastrointestinal endoscopy, with much of the early data showing significant benefits to use of AI during colonoscopy. However, there remain several challenges that may delay or hamper the widespread use of AI in the field.

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References

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  1. Kröner PT, Engels MM, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, et al. Artificial intelligence in gastroenterology: a state-of-the-art review. World J Gastroenterol. 2021;27:6794–824.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Berre CL, Sandborn WJ, Aridhi S, Devignes M-D, Fournier L, Smaïl-Tabbone M, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology. 2020;158:76-94.e2 (Elsevier).

    Article  PubMed  Google Scholar 

  3. 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 Cancer J Clin. 2018;68:394–424.

    Article  PubMed  Google Scholar 

  4. Corley DA, Jensen CD, Marks AR, Zhao WK, Lee JK, Doubeni CA, et al. Adenoma detection rate and risk of colorectal cancer and death. N Engl J Med. 2014;370:1298–306 (Massachusetts Medical Society).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zhao S, Wang S, Pan P, Xia T, Chang X, Yang X, et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology. 2019;156:1661-1674.e11 (Elsevier).

    Article  PubMed  Google Scholar 

  6. le Clercq CMC, Bouwens MWE, Rondagh EJA, Bakker CM, Keulen ETP, de Ridder RJ, et al. Postcolonoscopy colorectal cancers are preventable: a population-based study. Gut. 2014;63:957–63 (BMJ Publishing Group).

    Article  PubMed  Google Scholar 

  7. Robertson DJ, Lieberman DA, Winawer SJ, Ahnen DJ, Baron JA, Schatzkin A, et al. Colorectal cancers soon after colonoscopy: a pooled multicohort analysis. Gut. 2014;63:949–56 (BMJ Publishing Group).

    Article  PubMed  Google Scholar 

  8. Aslanian HR, Shieh FK, Chan FW, Ciarleglio MM, Deng Y, Rogart JN, et al. Nurse observation during colonoscopy increases polyp detection: a randomized prospective study. Am J Gastroenterol. 2013;108:166–72.

    Article  PubMed  Google Scholar 

  9. Maroulis DE, Iakovidis DK, Karkanis SA, Karras DA. CoLD: a versatile detection system for colorectal lesions in endoscopy video-frames. Comput Methods Programs Biomed. 2003;70:151–66.

    Article  CAS  PubMed  Google Scholar 

  10. Iakovidis DK, Maroulis DE, Karkanis SA. An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy. Comput Biol Med. 2006;36:1084–103.

    Article  PubMed  Google Scholar 

  11. Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, et al. Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy. Gastroenterology. 2018;155:1069-1078.e8 (Elsevier).

    Article  PubMed  Google Scholar 

  12. •• Wang P, Berzin TM, Glissen Brown JR, Bharadwaj S, Becq A, Xiao X, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut. 2019;68:1813–9. This was the first randomized controlled trial evaluating CADe in colonoscopy, showing a statistically significant benefit with AI.

    Article  PubMed  Google Scholar 

  13. • Repici A, Badalamenti M, Maselli R, Correale L, Radaelli F, Rondonotti E, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology. 2020;159:512-520.e7. This was the first randomized trial from a western country with high baseline ADR. This study showed a statistically significant increase in ADR in the AI group, despite a high baseline ADR in the control group.

    Article  PubMed  Google Scholar 

  14. • Glissen Brown JR, Mansour NM, Wang P, Chuchuca MA, Minchenberg SB, Chandnani M, et al. Deep learning computer-aided polyp detection reduces adenoma miss rate: a United States multi-center randomized tandem colonoscopy study (CADeT-CS Trial). Clin Gastroenterol Hepatol. 2022;20:1499-1507.e4. (Elsevier). This was the first randomized controlled trial on CADe from the United States. This study showed lower adenoma miss rate with use of CADe.

    Article  CAS  PubMed  Google Scholar 

  15. • Wallace MB, Sharma P, Bhandari P, East J, Antonelli G, Lorenzetti R, et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology. 2022;163:295-304.e5. (Elsevier). Important study from the US and Europe that showed that CADe led to a 50% reduction in adenoma miss rate.

    Article  PubMed  Google Scholar 

  16. Shaukat A, Lichtenstein DR, Somers SC, Chung DC, Perdue DG, Gopal M, et al. Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy: a randomized trial. Gastroenterology Elsevier. 2022;163:732–41.

    Article  Google Scholar 

  17. Shah S, Park N, Chehade NEH, Chahine A, Monachese M, Tiritilli A, et al. Effect of computer-aided colonoscopy on adenoma miss rates and polyp detection: a systematic review and meta-analysis. J Gastroenterol Hepatol [Internet]. [cited 2023 Jan 16];n/a. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/jgh.16059.

  18. Aziz M, Haghbin H, Sayeh W, Alfatlawi H, Gangwani MK, Sohail AH, et al. Comparison of artificial intelligence with other interventions to improve adenoma detection rate for colonoscopy: a network meta-analysis. J Clin Gastroenterol [Internet]. 2022 [cited 2023 Jan 16];Publish Ahead of Print. Available from: https://journals.lww.com/10.1097/MCG.0000000000001813.

  19. • Wang P, Liu X, Berzin TM, Glissen Brown JR, Liu P, Zhou C, et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020;5:343–51. This is the only double-blind study on CADe using a sham system. The study showed a statistically significant benefit with CADe.

    Article  PubMed  Google Scholar 

  20. Liu W-N, Zhang Y-Y, Bian X-Q, Wang L-J, Yang Q, Zhang X-D, et al. Study on detection rate of polyps and adenomas in artificial-intelligence-aided colonoscopy. Saudi J Gastroenterol. 2020;26:13.

    Article  PubMed  Google Scholar 

  21. Wang P, Liu P, Glissen Brown JR, Berzin TM, Zhou G, Lei S, et al. Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs routine white-light colonoscopy in a prospective tandem study. Gastroenterology. 2020;159:1252-1261.e5.

    Article  PubMed  Google Scholar 

  22. Kamba S, Tamai N, Saitoh I, Matsui H, Horiuchi H, Kobayashi M, et al. Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence: a multicenter randomized controlled trial. J Gastroenterol. 2021;56:746–57.

    Article  PubMed  Google Scholar 

  23. Xu H, Tang RSY, Lam TYT, Zhao G, Lau JYW, Liu Y, et al. Artificial intelligence–assisted colonoscopy for colorectal cancer screening: a multicenter randomized controlled trial. Clin Gastroenterol Hepatol [Internet]. Elsevier; 2022 [cited 2023 Jan 16];0. Available from: https://www.cghjournal.org/article/S1542-3565(22)00673-5/fulltext.

  24. Repici A, Spadaccini M, Antonelli G, Correale L, Maselli R, Galtieri PA, et al. Artificial intelligence and colonoscopy experience: lessons from two randomised trials. Gut. 2022;71:757–65.

    Article  PubMed  Google Scholar 

  25. Levy I, Bruckmayer L, Klang E, Ben-Horin S, Kopylov U. Artificial intelligence-aided colonoscopy does not increase adenoma detection rate in routine clinical practice. Am J Gastroenterol. 2022;117:1871–3.

    Article  PubMed  Google Scholar 

  26. • Ladabaum U, Shepard J, Weng Y, Desai M, Singer SJ, Mannalithara A. Computer-aided detection of polyps does not improve colonoscopist performance in a pragmatic implementation trial. Gastroenterology [Internet]. 2022 [cited 2023 Jan 16]; Available from: https://www.sciencedirect.com/science/article/pii/S0016508522013889. This is an important recent study showing negative results for CADe. While it is not a randomized trial, it was a well-designed pragmatic implementation trial, and we need to consider the reasons why the results here were different than randomized trials.

  27. Rex DK, Kahi C, O’Brien M, Levin TR, Pohl H, Rastogi A, et al. The American Society for Gastrointestinal Endoscopy PIVI (Preservation and Incorporation of Valuable Endoscopic Innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2011;73:419–22.

    Article  PubMed  Google Scholar 

  28. Abu Dayyeh BK, Thosani N, Konda V, Wallace MB, Rex DK, Chauhan SS, et al. ASGE Technology Committee systematic review and meta-analysis assessing the ASGE PIVI thresholds for adopting real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2015;81:502.e1-502.e16.

    Article  PubMed  Google Scholar 

  29. Tischendorf JJW, Gross S, Winograd R, Hecker H, Auer R, Behrens A, et al. Computer-aided classification of colorectal polyps based on vascular patterns: a pilot study. Endoscopy. © Georg Thieme Verlag KG Stuttgart New York; 2010;203–7.

  30. Kominami Y, Yoshida S, Tanaka S, Sanomura Y, Hirakawa T, Raytchev B, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc. 2016;83:643–9.

    Article  PubMed  Google Scholar 

  31. •• Mori Y, Kudo S, Misawa M, Saito Y, Ikematsu H, Hotta K, et al. Real-time use of artificial intelligence in identification of diminutive polyps during colonoscopy. Ann Intern Med. 2018;169:357–66. (American College of Physicians). Large prospective study showing benefit of CADx for polyp characterization.

    Article  PubMed  Google Scholar 

  32. Byrne MF, Chapados N, Soudan F, Oertel C, Pérez ML, Kelly R, et al. Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model. Gut. 2019;68:94–100 (BMJ Publishing Group).

    Article  PubMed  Google Scholar 

  33. • Hassan C, Balsamo G, Lorenzetti R, Zullo A, Antonelli G. Artificial intelligence allows leaving-in-situ colorectal polyps. Clin Gastroenterol Hepatol. 2022;20:2505-2513.e4. (Elsevier). Prospective study showing CADx could support resect and discard and diagnose and leave strategies using current standard colonoscopy technology.

    Article  PubMed  Google Scholar 

  34. Chaptini L, Chaaya A, Depalma F, Hunter K, Peikin S, Laine L. Variation in polyp size estimation among endoscopists and impact on surveillance intervals. Gastrointestinal Endoscopy. 2014;80:652–9 (Elsevier).

    Article  PubMed  Google Scholar 

  35. Rex DK, Rabinovitz R. Variable interpretation of polyp size by using open forceps by experienced colonoscopists. Gastrointestinal Endoscopy. 2014;79:402–7 (Elsevier).

    Article  PubMed  Google Scholar 

  36. Abdelrahim M, Saiga H, Maeda N, Hossain E, Ikeda H, Bhandari P. Automated sizing of colorectal polyps using computer vision. Gut. 2022;71:7–9.

    Article  PubMed  Google Scholar 

  37. Kwak MS, Cha JM, Jeon JW, Yoon JY, Park JW. Artificial intelligence-based measurement outperforms current methods for colorectal polyp size measurement. Dig Endosc. 2022;34:1188–95.

    Article  PubMed  Google Scholar 

  38. Desai M, Rex DK, Bohm ME, Davitkov P, DeWitt JM, Fischer M, et al. Impact of withdrawal time on adenoma detection rate: results from a prospective multicenter trial. Gastrointest Endosc. 2022;S0016–5107(22):02039–49.

    Google Scholar 

  39. Barua I, Misawa M, Glissen Brown JR, Walradt T, Kudo S, Sheth SG, et al. Speedometer for withdrawal time monitoring during colonoscopy: a clinical implementation trial. Scand J Gastroenterol. 2022;0:1–7 (Taylor & Francis).

    CAS  Google Scholar 

  40. Gong D, Wu L, Zhang J, Mu G, Shen L, Liu J, et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020;5:352–61.

    Article  PubMed  Google Scholar 

  41. Su J-R, Li Z, Shao X-J, Ji C-R, Ji R, Zhou R-C, et al. Impact of a real-time automatic quality control system on colorectal polyp and adenoma detection: a prospective randomized controlled study (with videos). Gastrointest Endosc. 2020;91:415-424.e4.

    Article  PubMed  Google Scholar 

  42. Aniwan S, Mekritthikrai K, Kerr SJ, Tiankanon K, Vandaungden K, Sritunyarat Y, et al. Computer-aided detection, mucosal exposure device, their combination, and standard colonoscopy for adenoma detection: a randomized controlled trial. Gastrointest Endosc [Internet]. 2022 [cited 2023 Jan 16]; Available from: https://www.sciencedirect.com/science/article/pii/S0016510722020211.

  43. Hassan C, Spadaccini M, Iannone A, Maselli R, Jovani M, Chandrasekar VT, et al. Performance of artificial intelligence in colonoscopy for adenoma and polyp detection: a systematic review and meta-analysis. Gastrointest Endosc. 2021;93:77-85.e6.

    Article  PubMed  Google Scholar 

  44. Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digital Health. 2022;4:e436–44.

    Article  CAS  PubMed  Google Scholar 

  45. Rex DK, Berzin TM, Mori Y. Artificial intelligence improves detection at colonoscopy: why aren’t we all already using it? Gastroenterology. 2022;163:35–7 (Elsevier).

    Article  PubMed  Google Scholar 

  46. Mori Y, Wang P, Løberg M, Misawa M, Repici A, Spadaccini M, et al. Impact of artificial intelligence on colonoscopy surveillance after polyp removal: a pooled analysis of randomized trials. Clin Gastroenterol Hepatol [Internet]. Elsevier; 2022 [cited 2023 Jan 16];0. Available from: https://www.cghjournal.org/article/S1542-3565(22)00818-7/fulltext.

  47. Kamitani Y, Nonaka K, Isomoto H. Current status and future perspectives of artificial intelligence in colonoscopy. J Clin Med. 2022;11:2923.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Mori Y, East JE, Hassan C, Halvorsen N, Berzin TM, Byrne M, et al. Benefits and challenges in implementation of artificial intelligence in colonoscopy: world endoscopy organization position statement. Dig Endosc [Internet]. [cited 2023 Feb 9];n/a. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/den.14531.

  49. Barua I, Wieszczy P, Kudo S, Misawa M, Holme Ø, Gulati S, et al. Real-time artificial intelligence-based optical diagnosis of neoplastic polyps during colonoscopy. NEJM Evidence. 2022;1:EVIDoa2200003 (Massachusetts Medical Society).

    Article  Google Scholar 

  50. Rondonotti E, Hassan C, Tamanini G, Antonelli G, Andrisani G, Leonetti G, et al. Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the artificial intelligence BLI characterization (ABC) study. Endoscopy. 2022;14–22. (Georg Thieme Verlag KG).

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  1. Section of Gastroenterology and Hepatology, Baylor College of Medicine, 7200 Cambridge St., Suite 8B, Houston, TX, 77030, USA

    Nabil M. Mansour

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Correspondence to Nabil M. Mansour.

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Dr. Mansour has received research support from Wision A.I. (Shanghai, China), outside the submitted work.

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Mansour, N.M. Artificial Intelligence in Colonoscopy. Curr Gastroenterol Rep 25, 122–129 (2023). https://doi.org/10.1007/s11894-023-00872-x

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