journal article Open Access Apr 25, 2025

Artificial intelligence in inflammatory bowel disease

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Abstract
Abstract
Inflammatory bowel disease (IBD) is a complex condition influenced by various intestinal factors. Advances in next-generation sequencing, high-throughput omics, and molecular network technologies have significantly accelerated research in this field. The emergence of artificial intelligence (AI) has further enhanced the efficient utilization and interpretation of datasets, enabling the discovery of clinically actionable insights. AI is now extensively applied in gastroenterology, where it aids in endoscopic analyses, including the diagnosis of colorectal cancer, precancerous polyps, gastrointestinal inflammatory lesions, and bleeding. Additionally, AI supports clinicians in patient stratification, predicting disease progression and treatment responses, and adjusting treatment plans in a timely manner. This approach not only reduces healthcare costs but also improves patient health and safety. This review outlines the principles of AI, the current research landscape, and future directions for its applications in IBD, with the goal of advancing targeted treatment strategies.
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References
77
[1]
Aniwan "The epidemiology of inflammatory bowel disease in Asia and Asian immigrants to Western countries" United Eur Gastroenterol J (2022) 10.1002/ueg2.12350
[2]
Taghavi "Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases" Abdom Radiol (NY) (2021) 10.1007/s00261-020-02624-1
[3]
Da Rio "Artificial intelligence and inflammatory bowel disease: Where are we going?" World J Gastroenterol (2023) 10.3748/wjg.v29.i3.508
[4]
Evaluation in real-time use of artificial intelligence during colonoscopy to predict relapse of ulcerative colitis: a prospective study

Yasuharu Maeda, Shin-Ei Kudo, Noriyuki Ogata et al.

Gastrointestinal Endoscopy 2022 10.1016/j.gie.2021.10.019
[5]
Gottlieb "Central reading of ulcerative colitis clinical trial videos using neural networks" Gastroenterology (2021) 10.1053/j.gastro.2020.10.024
[6]
High-performance medicine: the convergence of human and artificial intelligence

Eric J. Topol

Nature Medicine 2019 10.1038/s41591-018-0300-7
[7]
Sharma "Artificial intelligence in intestinal polyp and colorectal cancer prediction" Cancer Lett (2023) 10.1016/j.canlet.2023.216238
[8]
Stafford "A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases" NPJ Digit Med (2020) 10.1038/s41746-020-0229-3
[9]
Haug "Artificial intelligence and machine learning in clinical medicine, 2023" N Engl J Med (2023) 10.1056/nejmra2302038
[10]
Integrated Metagenomics/Metaproteomics Reveals Human Host-Microbiota Signatures of Crohn's Disease

Alison R. Erickson, Brandi L. Cantarel, Regina Lamendella et al.

PLoS ONE 2012 10.1371/journal.pone.0049138
[11]
Isakov "Machine learning-based gene prioritization identifies novel candidate risk genes for inflammatory bowel disease" Inflamm Bowel Dis (2017) 10.1097/mib.0000000000001222
[12]
Annese "Genetics and epigenetics of IBD" Pharmacol Res (2020) 10.1016/j.phrs.2020.104892
[13]
Cheng "Integrative analysis of transcriptome-wide association study data and messenger RNA expression profiles identified candidate genes and pathways for inflammatory bowel disease" J Cell Biochem (2019) 10.1002/jcb.28744
[14]
Seyed Tabib "Big data in IBD: Big progress for clinical practice" Gut (2020) 10.1136/gutjnl-2019-320065
[15]
Chang "Artificial intelligence in inflammatory bowel disease endoscopy: Advanced development and new horizons" Gastroenterol Res Pract 2023 (2023)
[16]
Takenaka "Development and validation of a deep neural network for accurate evaluation of endoscopic images from patients with ulcerative colitis" Gastroenterology (2020) 10.1053/j.gastro.2020.02.012
[17]
Bossuyt "Automatic, computer-aided determination of endoscopic and histological inflammation in patients with mild to moderate ulcerative colitis based on red density" Gut (2020) 10.1136/gutjnl-2019-320056
[18]
Quénéhervé "Quantitative assessment of mucosal architecture using computer-based analysis of confocal laser endomicroscopy in inflammatory bowel diseases" Gastrointest Endosc (2019) 10.1016/j.gie.2018.08.006
[19]
Pennazio "Small-bowel capsule endoscopy and device-assisted enteroscopy for diagnosis and treatment of small-bowel disorders: European Society of Gastrointestinal Endoscopy (ESGE) Clinical Guideline" Endoscopy (2015) 10.1055/s-0034-1391855
[20]
Brodersen "Artificial intelligence-assisted analysis of pan-enteric capsule endoscopy in patients with suspected Crohn's disease: A study on diagnostic performance" J Crohns Colitis (2024) 10.1093/ecco-jcc/jjad131
[21]
Barash "Ulcer severity grading in video capsule images of patients with Crohn's disease: An ordinal neural network solution" Gastrointest Endosc (2021) 10.1016/j.gie.2020.05.066
[22]
Charisis "Potential of hybrid adaptive filtering in inflammatory lesion detection from capsule endoscopy images" World J Gastroenterol (2016) 10.3748/wjg.v22.i39.8641
[24]
Shi "Computed tomography enterography radiomics and machine learning for identification of Crohn's disease" BMC Med Imaging (2024) 10.1186/s12880-024-01480-5
[25]
Stidham "Assessing small bowel stricturing and morphology in Crohn's disease using semi-automated image analysis" Inflamm Bowel Dis (2020) 10.1093/ibd/izz196
[26]
Mahapatra "Semi-supervised and active learning for automatic segmentation of Crohn's disease" Med Image Comput Comput Assist Interv (2013)
[27]
Choi "Artificial intelligence in the pathology of gastric cancer" J Gastric Cancer (2023) 10.5230/jgc.2023.23.e25
[28]
Campanella "Clinical-grade computational pathology using weakly supervised deep learning on whole slide images" Nat Med (2019) 10.1038/s41591-019-0508-1
[29]
Vande Casteele "Utilizing deep learning to analyze whole slide images of colonic biopsies for associations between eosinophil density and clinicopathologic features in active ulcerative colitis" Inflamm Bowel Dis (2022) 10.1093/ibd/izab122
[30]
Furlanello "The development of artificial intelligence in the histological diagnosis of Inflammatory Bowel Disease (IBD-AI)" Dig Liver Dis (2025) 10.1016/j.dld.2024.05.033
[31]
Noguchi "Artificial intelligence program to predict p53 mutations in ulcerative colitis-associated cancer or dysplasia" Inflamm Bowel Dis (2022) 10.1093/ibd/izab350
[32]
Mihajlović "Machine learning based metagenomic prediction of inflammatory bowel disease" Stud Health Technol Inform (2021)
[33]
Manandhar "Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases" Am J Physiol Gastrointest Liver Physiol (2021) 10.1152/ajpgi.00360.2020
[34]
Gevers "The treatment-naive microbiome in new-onset Crohn's disease" Cell Host Microbe (2014) 10.1016/j.chom.2014.02.005
[35]
Seeley "Proteomic patterns of colonic mucosal tissues delineate Crohn's colitis and ulcerative colitis" Proteomics Clin Appl (2013) 10.1002/prca.201200107
[36]
Gao "Microbial genes outperform species and SNVs as diagnostic markers for Crohn's disease on multicohort fecal metagenomes empowered by artificial intelligence" Gut Microbes (2023) 10.1080/19490976.2023.2221428
[37]
Plevy "Combined serological, genetic, and inflammatory markers differentiate non-IBD, Crohn's disease, and ulcerative colitis patients" Inflamm Bowel Dis (2013) 10.1097/mib.0b013e318280b19e
[38]
Cao "Role of MiRNAs in inflammatory bowel disease" Dig Dis Sci (2017) 10.1007/s10620-017-4567-1
[39]
Hübenthal "Sparse modeling reveals miRNA signatures for diagnostics of inflammatory bowel disease" PLoS One (2015) 10.1371/journal.pone.0140155
[41]
Sutton "Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images" Sci Rep (2022) 10.1038/s41598-022-06726-2
[43]
Jahagirdar "Diagnostic accuracy of convolutional neural network-based machine learning algorithms in endoscopic severity prediction of ulcerative colitis: A systematic review and meta-analysis" Gastrointest Endosc (2023) 10.1016/j.gie.2023.04.2074
[44]
Yao "Fully automated endoscopic disease activity assessment in ulcerative colitis" Gastrointest Endosc (2021) 10.1016/j.gie.2020.08.011
[46]
Iacucci "Artificial intelligence enabled histological prediction of remission or activity and clinical outcomes in ulcerative colitis" Gastroenterology (2023) 10.1053/j.gastro.2023.02.031
[47]
Deep Learning Models Capture Histological Disease Activity in Crohn’s Disease and Ulcerative Colitis with High Fidelity

Dawid Rymarczyk, Weiwei Schultz, Adriana Borowa et al.

Journal of Crohn's and Colitis 2024 10.1093/ecco-jcc/jjad171
[48]
Stidham "Performance of a deep learning model vs human reviewers in grading endoscopic disease severity of patients with ulcerative colitis" JAMA Netw Open (2019) 10.1001/jamanetworkopen.2019.3963
[49]
Ozawa "Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis" Gastrointest Endosc (2019) 10.1016/j.gie.2018.10.020
[50]
Reddy "Predicting and explaining inflammation in Crohn's disease patients using predictive analytics methods and electronic medical record data" Health Informatics J (2019) 10.1177/1460458217751015

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Cited By
4
World Journal of Gastroenterology
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4
Citations
77
References
Details
Published
Apr 25, 2025
Vol/Issue
31(4)
Pages
197-205
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Cite This Article
Jiaxuan Ran, Mingxia Zhou, Hongtao Wen (2025). Artificial intelligence in inflammatory bowel disease. Saudi Journal of Gastroenterology, 31(4), 197-205. https://doi.org/10.4103/sjg.sjg_46_25