Skip to main content

Advertisement

SpringerLink
Log in

Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review

  • Systematic review
  • Published:
Indian Journal of Gastroenterology Aims and scope Submit manuscript

Abstract

Background and Objectives

In spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas.

Methods

We searched the PubMed and Embase database using keywords (“artificial intelligence” OR “machine learning” OR “computer-aided” OR “convolutional neural network”) AND (“inflammatory bowel disease” OR “ulcerative colitis” OR “Crohn’s”) AND (“endoscopy” or “colonoscopy” or “capsule endoscopy” or “device assisted enteroscopy”) between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist.

Results

The accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn’s disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning.

Conclusion

AI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data availability

The data associated with the paper are included in the manuscript. Additional data is available on request to the corresponding author.

References

  1. Tontini GE, Rimondi A, Vernero M, et al. Artificial intelligence in gastrointestinal endoscopy for inflammatory bowel disease: a systematic review and new horizons. Therap Adv Gastroenterol. 2021;14:17562848211017730.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Mori Y, East JE, Hassan C, et al. Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement. Dig Endosc. 2023;35:422–9.

    Article  PubMed  Google Scholar 

  3. Ting Sim JZ, Fong QW, Huang W, Tan CH. Machine learning in medicine: what clinicians should know. Singapore Med J. 2023;64:91–7.

    PubMed  Google Scholar 

  4. Sasaki Y, Hada R, Munakata A. Computer-aided grading system for endoscopic severity in patients with ulcerative colitis. Dig Endosc. 2003;15:206–9.

    Article  Google Scholar 

  5. Norgeot B, Quer G, Beaulieu-Jones BK, et al. Minimum information about clinical artificial intelligence modeling: the MICLAIM checklist. Nat Med. 2020;26:1320–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Syst Rev. 2021;10:89.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Sutton RT, Zai Ane OR, Goebel R, Baumgart DC. Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images. Sci Rep. 2022;12:2748.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  8. Sharma A, Kumar R, Garg P. Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images. Int J Med Inform. 2023;177:105142.

    Article  PubMed  Google Scholar 

  9. Guimarães P, Finkler H, Reichert MC, et al. Artificial-intelligence-based decision support tools for the differential diagnosis of colitis. Eur J Clin Invest. 2023;53:e13960.

    Article  PubMed  Google Scholar 

  10. Quénéhervé L, David G, Bourreille A, et al. Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases. Gastrointest Endosc. 2019;89:626–36.

    Article  PubMed  Google Scholar 

  11. Chierici M, Puica N, Pozzi M, et al. Automatically detecting Crohn’s disease and ulcerative colitis from endoscopic imaging. BMC Med Inform Decis Mak. 2022;22Suppl 6:300.

  12. Ruan G, Qi J, Cheng Y, et al. Development and validation of a deep neural network for accurate identification of endoscopic images from patients with ulcerative colitis and Crohn’s disease. Front Med (Lausanne). 2022;9:854677.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang L, Chen L, Wang X, et al. Development of a convolutional neural network-based colonoscopy image assessment model for differentiating Crohn’s disease and ulcerative colitis. Front Med (Lausanne). 2022;9:789862.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sachan A, Kakadiya R, Mishra S, et al. Artificial intelligence for discrimination of Crohn's disease and gastrointestinal tuberculosis: A systematic review. J Gastroenterol Hepatol. 2023. https://doi.org/10.1111/jgh.16430.

  15. Tong Y, Lu K, Yang Y, et al. Can natural language processing help differentiate inflammatory intestinal diseases in China? Models applying random forest and convolutional neural network approaches. BMC Med Inform Decis Mak. 2020;20:248.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kim JM, Kang JG, Kim S, Cheon JH. Deep-learning system for real-time differentiation between Crohn’s disease, intestinal Behçet’s disease, and intestinal tuberculosis. J Gastroenterol Hepatol. 2021;36:2141–8.

    Article  PubMed  Google Scholar 

  17. Lu Y, Chen Y, Peng X, et al. Development and validation of a new algorithm model for differential diagnosis between Crohn’s disease and intestinal tuberculosis: a combination of laboratory, imaging and endoscopic characteristics. BMC Gastroenterol. 2021;21:291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Lu K, Tong Y, Yu S, et al. Building a trustworthy AI differential diagnosis application for Crohn’s disease and intestinal tuberculosis. BMC Med Inform Decis Mak. 2023;23:160.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Charisis VS, Hadjileontiadis LJ. Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images. World J Gastroenterol. 2016;22:8641–57.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Aoki T, Yamada A, Aoyama K, et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc. 2019;89:357–63.e2.

    Article  PubMed  Google Scholar 

  21. Klang E, Barash Y, Margalit RY, et al. Deep learning algorithms for automated detection of Crohn’s disease ulcers by video capsule endoscopy. Gastrointest Endosc. 2020;91:606–13.e2.

    Article  PubMed  Google Scholar 

  22. Barash Y, Azaria L, Soffer S, et al. Ulcer severity grading in video capsule images of patients with Crohn’s disease: an ordinal neural network solution. Gastrointest Endosc. 2021;93:187–92.

    Article  PubMed  Google Scholar 

  23. Klang E, Grinman A, Soffer S, et al. Automated detection of Crohn’s disease intestinal strictures on capsule endoscopy images using deep neural networks. J Crohns Colitis. 2021;15:749–56.

    Article  PubMed  Google Scholar 

  24. Majtner T, Brodersen JB, Herp J, et al. A deep learning framework for autonomous detection and classification of Crohn’s disease lesions in the small bowel and colon with capsule endoscopy. Endosc Int Open. 2021;9:E1361–70.

    Article  PubMed  PubMed Central  Google Scholar 

  25. de Maissin A, Vallée R, Flamant M, et al. Multi-expert annotation of Crohn’s disease images of the small bowel for automatic detection using a convolutional recurrent attention neural network. Endosc Int Open. 2021;9:E1136–44.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Ferreira JPS, de Mascarenhas SM, Afonso JPL, et al. Identification of ulcers and erosions by the novel PillCam™ Crohn’s capsule using a convolutional neural network: a multicentre pilot study. J Crohns Colitis. 2022;16:169–72.

    Article  PubMed  Google Scholar 

  27. Higuchi N, Hiraga H, Sasaki Y, et al. Automated evaluation of colon capsule endoscopic severity of ulcerative colitis using ResNet50. PLoS One. 2022;17:e0269728.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Kratter T, Shapira N, Lev Y, et al. Deep learning multi-domain model provides accurate detection and grading of mucosal ulcers in different capsule endoscopy types. Diagnostics (Basel). 2022;12:2490.

  29. Ribeiro T, Mascarenhas M, Afonso J, et al. Artificial intelligence and colon capsule endoscopy: automatic detection of ulcers and erosions using a convolutional neural network. J Gastroenterol Hepatol. 2022;37:2282–8.

    Article  PubMed  Google Scholar 

  30. Brodersen JB, Jensen MD, Leenhardt R, et al. Artificial intelligence-assisted analysis of pan-enteric capsule endoscopy in patients with suspected Crohn's disease. A study on diagnostic performance. J Crohns Colitis. 2023;1:jjad131. https://doi.org/10.1093/ecco-jcc/jjad131.

    Article  Google Scholar 

  31. Ozawa T, Ishihara S, Fujishiro M, et al. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis. Gastrointest Endosc. 2019;89:416–21.e1.

    Article  PubMed  Google Scholar 

  32. Stidham RW, Liu W, Bishu S, et al. Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis. JAMA Netw Open. 2019;2:e193963.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bossuyt P, Nakase H, Vermeire S, et al. Automatic, computer-aided determination of endoscopic and histological inflammation in patients with mild to moderate ulcerative colitis based on red density. Gut. 2020;69:1778–86.

    Article  CAS  PubMed  Google Scholar 

  34. Takenaka K, Ohtsuka K, Fujii T, et al. Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis. Gastroenterology. 2020;158:2150–7.

    Article  PubMed  Google Scholar 

  35. Bhambhvani HP, Zamora A. Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis. Eur J Gastroenterol Hepatol. 2021;33:645–9.

    Article  CAS  PubMed  Google Scholar 

  36. Gottlieb K, Requa J, Karnes W, et al. Central reading of ulcerative colitis clinical trial videos using neural networks. Gastroenterology. 2021;160:710–9.e2.

    Article  PubMed  Google Scholar 

  37. Gutierrez Becker B, Arcadu F, Thalhammer A, et al. Training and deploying a deep learning model for endoscopic severity grading in ulcerative colitis using multicenter clinical trial data. Ther Adv Gastrointest Endosc. 2021;14:2631774521990623.

    PubMed  PubMed Central  Google Scholar 

  38. Huang TY, Zhan SQ, Chen PJ, et al. Accurate diagnosis of endoscopic mucosal healing in ulcerative colitis using deep learning and machine learning. J Chin Med Assoc. 2021;84:678–81.

    Article  PubMed  Google Scholar 

  39. Yao H, Najarian K, Gryak J, et al. Fully automated endoscopic disease activity assessment in ulcerative colitis. Gastrointest Endosc. 2021;93:728–36.e1.

    Article  PubMed  Google Scholar 

  40. Lo B, Liu Z, Bendtsen F, et al. High accuracy in classifying endoscopic severity in ulcerative colitis using convolutional neural network. Am J Gastroenterol. 2022;117:1648–54.

    Article  PubMed  Google Scholar 

  41. Luo X, Zhang J, Li Z, Yang R. Diagnosis of ulcerative colitis from endoscopic images based on deep learning. Biomed Signal Process Control. 2022;73:103443.

    Article  Google Scholar 

  42. Patel M, Gulati S, Iqbal F, Hayee B. Rapid development of accurate artificial intelligence scoring for colitis disease activity using applied data science techniques. Endosc Int Open. 2022;10:E539–43.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Schwab E, Cula GO, Standish K, et al. Automatic estimation of ulcerative colitis severity from endoscopy videos using ordinal multi-instance learning. Comput Methods Biomech Biomedical Eng Imaging Visu. 2022;10:425–33.

    Article  Google Scholar 

  44. Takenaka K, Fujii T, Kawamoto A, et al. Deep neural network for video colonoscopy of ulcerative colitis: a cross-sectional study. Lancet Gastroenterol Hepatol. 2022;7:230–7.

    Article  PubMed  Google Scholar 

  45. Turan M, Durmus F. UC-NfNet: deep learning-enabled assessment of ulcerative colitis from colonoscopy images. Med Image Anal. 2022;82:102587.

    Article  PubMed  Google Scholar 

  46. Fan Y, Mu R, Xu H, et al. Novel deep learning-based computer-aided diagnosis system for predicting inflammatory activity in ulcerative colitis. Gastrointest Endosc. 2023;97:335–46.

    Article  PubMed  Google Scholar 

  47. Iacucci M, Cannatelli R, Parigi TL, et al. A virtual chromoendoscopy artificial intelligence system to detect endoscopic and histologic activity/remission and predict clinical outcomes in ulcerative colitis. Endoscopy. 2023;55:332–41.

    Article  PubMed  Google Scholar 

  48. Kim JE, Choi YH, Lee YC, et al. Deep learning model for distinguishing Mayo endoscopic subscore 0 and 1 in patients with ulcerative colitis. Sci Rep. 2023;13:11351.

    Article  CAS  PubMed  PubMed Central  ADS  Google Scholar 

  49. Polat G, Kani HT, Ergenc I, et al. Improving the computer-aided estimation of ulcerative colitis severity according to Mayo endoscopic score by using regression-based deep learning. Inflamm Bowel Dis. 2023;29:1431–9.

    Article  PubMed  Google Scholar 

  50. Qi J, Ruan G, Ping Y, et al. Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis. Therap Adv Gastroenterol. 2023;16:17562848231170944.

    Article  Google Scholar 

  51. Takabayashi K, Kobayashi T, Matsuoka K, et al. Artificial intelligence quantifying endoscopic severity of ulcerative colitis in gradation scale. Dig Endosc. 2023. https://doi.org/10.1111/den.14677.

  52. Wang G, Zhang S, Li J, et al. CB-HRNet: a class-balanced high-resolution network for the evaluation of endoscopic activity in patients with ulcerative colitis. Clin Transl Sci. 2023;16:1421–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Stidham RW, Cai L, Cheng S, et al. Using computer vision to improve endoscopic disease quantification in therapeutic clinical trials of ulcerative colitis. Gastroenterology. 2024 Jan;166:155–167.e2.

  54. Guez I, Focht G, Greer MC, et al. Development of a multimodal machine-learning fusion model to non-invasively assess ileal Crohn’s disease endoscopic activity. Comput Methods Programs Biomed. 2022;227:107207.

    Article  PubMed  Google Scholar 

  55. Li X, Yan L, Wang X, et al. Predictive models for endoscopic disease activity in patients with ulcerative colitis: practical machine learning-based modeling and interpretation. Front Med (Lausanne). 2022;9:1043412.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Gavrilescu O, Popa IV, Dranga M, et al. Laboratory data and IBDQ-effective predictors for the non-invasive machine-learning-based prediction of endoscopic activity in ulcerative colitis. J Clin Med. 2023;12:3609.

  57. Udristoiu AL, Stefanescu D, Gruionu G, et al. Deep learning algorithm for the confirmation of mucosal healing in Crohn’s disease, based on confocal laser endomicroscopy images. J Gastrointestin Liver Dis. 2021;30:59–65.

    Article  PubMed  Google Scholar 

  58. Martins M, Mascarenhas M, Afonso J, et al. Deep-learning and device-assisted enteroscopy: automatic panendoscopic detection of ulcers and erosions. Medicina (Kaunas). 2023;59:172.

  59. Maeda Y, Kudo SE, Mori Y, et al. Fully automated diagnostic system with artificial intelligence using endocytoscopy to identify the presence of histologic inflammation associated with ulcerative colitis (with video). Gastrointest Endosc. 2019;89:408–15.

    Article  PubMed  Google Scholar 

  60. Bossuyt P, De Hertogh G, Eelbode T, et al. Computer-aided diagnosis with monochromatic light endoscopy for scoring histologic remission in ulcerative colitis. Gastroenterology. 2021;160:23–5.

    Article  PubMed  Google Scholar 

  61. Gui X, Bazarova A, Del Amor R, et al. PICaSSO Histologic Remission Index (PHRI) in ulcerative colitis: development of a novel simplified histological score for monitoring mucosal healing and predicting clinical outcomes and its applicability in an artificial intelligence system. Gut. 2022;71:889–98.

    Article  PubMed  Google Scholar 

  62. Iacucci M, Jeffery L, Acharjee A, et al. Computer-aided imaging analysis of probe-based confocal laser endomicroscopy with molecular labeling and gene expression identifies markers of response to biological therapy in IBD patients: the Endo-Omics Study. Inflamm Bowel Dis. 2023;29:1409–20.

    Article  PubMed  Google Scholar 

  63. Iacucci M, Parigi TL, Del Amor R, et al. Artificial intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis. Gastroenterology. 2023;164:1180–8.e2.

    Article  PubMed  Google Scholar 

  64. Guerrero Vinsard D, Fetzer JR, Agrawal U, et al. Development of an artificial intelligence tool for detecting colorectal lesions in inflammatory bowel disease. iGIE. 2023;2:91–101.e6.

    Article  Google Scholar 

  65. Yamamoto S, Kinugasa H, Hamada K, et al. The diagnostic ability to classify neoplasias occurring in inflammatory bowel disease by artificial intelligence and endoscopists: a pilot study. J Gastroenterol Hepatol. 2022;37:1610–6.

    Article  CAS  PubMed  Google Scholar 

  66. Fukunaga S, Kusaba Y, Ohuchi A, et al. Is artificial intelligence a superior diagnostician in ulcerative colitis? Endoscopy. 2021;53:E75–6.

    Article  PubMed  Google Scholar 

  67. Pal P, Ramchandani M, Patel R, et al. Role of ultra-high definition endoscopy (endomicroscopy and endocytoscopy) and real-time histologic examination in inflammatory bowel disease: Scoping review. Dig Endosc. 2023. https://doi.org/10.1111/den.14659.

  68. Keswani RN, Byrd D, Garcia Vicente F, et al. Amalgamation of cloud-based colonoscopy videos with patient-level metadata to facilitate large-scale machine learning. Endosc Int Open. 2021;9:E233–8.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Hou JK, Chang M, Nguyen T, et al. Automated identification of surveillance colonoscopy in inflammatory bowel disease using natural language processing. Dig Dis Sci. 2013;58:936–41.

    Article  CAS  PubMed  Google Scholar 

  70. Mazumdar S, Sinha S, Jha S, Jagtap B. Computer-aided automated diminutive colonic polyp detection in colonoscopy by using deep machine learning system; first indigenous algorithm developed in India. Indian J Gastroenterol. 2023;42:226–32.

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Medical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India

    Partha Pal, Kanapuram Pooja, Zaheer Nabi, Rajesh Gupta, Manu Tandan & Nageshwar Reddy

  2. Surgical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500 082, India

    Guduru Venkat Rao

Authors
  1. Partha Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kanapuram Pooja
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zaheer Nabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rajesh Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manu Tandan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guduru Venkat Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Nageshwar Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Concept and design: PP; administrative support: NR, MT, RG; provision of study material/patients: PP, KP, ZN, GVR; acquisition of data: KP, PP; performing literature search: PP, KP; data analysis and interpretation: PP, KP; preparation of initial draft: PP, KP, ZN, GVR; critical revision of the manuscript: MT, GVR, RG, NR, PP, KP, ZN; important intellectual inputs and revision: MT, ZN, RG, NR; manuscript writing: all authors; approval of final manuscript: all authors.

Corresponding author

Correspondence to Partha Pal.

Ethics declarations

Conflict of interest

PP, KP, ZN, RG, MT, GVR and NR declare no competing interests.

Ethical approval

Institutional review board approval was exempted as the article is a systematic review of previously published literature.

Consent to participate

Informed consent is not applicable given it is a systematic review of published studies.

Disclaimer

The authors are solely responsible for the data and the contents of the paper. In no way, the Honorary Editor-in-Chief, Editorial Board Members, the Indian Society of Gastroenterology or the printer/publishers are responsible for the results/findings and content of this article.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Below is the link to the electronic supplementary material.

Supplementary file1 (DOCX 19 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pal, P., Pooja, K., Nabi, Z. et al. Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review. Indian J Gastroenterol 43, 172–187 (2024). https://doi.org/10.1007/s12664-024-01531-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12664-024-01531-3

Keywords

Search

Navigation