Artificial intelligence applications in inflammatory bowel disease: Emerging technologies and future directions
Open accessAbstract
Inflammatory bowel disease (IBD) is a complex and multifaceted disorder of the gastrointestinal tract that is increasing in incidence worldwide and associated with significant morbidity. The rapid accumulation of large datasets from electronic health records, high-definition multi-omics (including genomics, proteomics, transcriptomics, and metagenomics), and imaging modalities (endoscopy and endomicroscopy) have provided powerful tools to unravel novel mechanistic insights and help address unmet clinical needs in IBD. Although the application of artificial intelligence (AI) methods has facilitated the analysis, integration, and interpretation of large datasets in IBD, significant heterogeneity in AI methods, datasets, and clinical outcomes and the need for unbiased prospective validations studies are current barriers to incorporation of AI into clinical practice. The purpose of this review is to summarize the most recent advances in the application of AI and machine learning technologies in the diagnosis and risk prediction, assessment of disease severity, and prediction of clinical outcomes in patients with IBD.
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There is significant heterogeneity in the datasets AI algorithms are trained with within algorithm types themselves.
“Currently, there is significant heterogeneity in terms of the datasets AI algorithms are trained with within algorithm types themselves13,14.”
From Bias in AI-based models for medical applications: challenges and mitigation strategies (2023) · cites this paper as [13]
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Ng, S. C., Shi, H. Y., Hamidi, N., Underwood, F. E., Tang, W., Benchimol, E. I., Panaccione, R., Ghosh, S., Wu, J. C. Y., Chan, F. K. L., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32448-0 · Cited by 6,087 · see citing works
Daperno, M., D’Haens, G., Assche, G. V., Baert, F., Bulois, P., Maunoury, V., Sostegni, R., Rocca, R., Pera, A., Gevers, A., et al. (2004) · Gastrointestinal Endoscopy · doi:10.1016/s0016-5107(04)01878-4 · Cited by 1,779 · see citing works
Lewis, J. D., Chuai, S., Nessel, L. A., Lichtenstein, G. R., Aberra, F. N., & Ellenberg, J. H. (2008) · Inflammatory Bowel Diseases · doi:10.1002/ibd.20520 · Cited by 965 · see citing works
Environmental triggers in IBD: a review of progress and evidence
Ananthakrishnan, A. N., Bernstein, C. N., Iliopoulos, D., Macpherson, A., Neurath, M. F., Ali, R. A. R., Vavricka, S. R., & Fiocchi, C. (2018) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.136 · Cited by 948 · see citing works
Wang, P., Berzin, T. M., Glissen Brown, J. R., Bharadwaj, S., Becq, A., Xiao, X., Liu, P., Li, L., Song, Y., Zhang, D., et al. (2019) · Gut · doi:10.1136/gutjnl-2018-317500 · Cited by 865 · see citing works
Current and emerging therapeutic targets for IBD
Neurath, M. F. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2016.208 · Cited by 700 · see citing works
Artificial intelligence, machine learning and health systems
Panch, T., Peter Szolovits, B., & Atun, R. A. (2018) · Journal of Global Health · doi:10.7189/jogh.08.020303 · Cited by 637 · see citing works
Gillen, C. D., Walmsley, R. S., Prior, P., Andrews, H. A., & Allan, R. N. (1994) · Gut · doi:10.1136/gut.35.11.1590 · Cited by 515 · see citing works
Wang, W., & Siau, K. (2019) · Journal of Database Management · doi:10.4018/jdm.2019010104 · Cited by 452 · see citing works
The IBD interactome: an integrated view of aetiology, pathogenesis and therapy
de Souza, H. S. P., Fiocchi, C., & Iliopoulos, D. (2017) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2017.110 · Cited by 447 · see citing works
Statistical challenges of high-dimensional data
Johnstone, I. M., & Titterington, D. M. (2009) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2009.0159 · Cited by 420 · see citing works
Wang, P., Liu, X., Berzin, T. M., Glissen Brown, J. R., Liu, P., Zhou, C., Lei, L., Li, L., Guo, Z., Lei, S., et al. (2020) · The Lancet Gastroenterology & Hepatology · doi:10.1016/s2468-1253(19)30411-x · Cited by 415 · see citing works
Jones, J., Loftus, E. V., Panaccione, R., Chen, L., Peterson, S. E., McConnell, J. P., Baudhuin, L. M., Hanson, K., Feagan, B. G., Harmsen, S. W., et al. (2008) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2008.06.010 · Cited by 414 · see citing works
Inflammatory bowel disease: the role of environmental factors
Danese, S., Sans, M., & Fiocchi, C. (2004) · Autoimmunity Reviews · doi:10.1016/j.autrev.2004.03.002 · Cited by 406 · see citing works
Artificial Intelligence-Assisted Polyp Detection for Colonoscopy: Initial Experience
Misawa, M., Kudo, S., Mori, Y., Cho, T., Kataoka, S., Yamauchi, A., Ogawa, Y., Maeda, Y., Takeda, K., Ichimasa, K., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.04.003 · Cited by 395 · see citing works
Predicting the Crohnʼs disease activity index from the harvey-bradshaw index
Best, W. R. (2006) · Inflammatory Bowel Diseases · doi:10.1097/01.mib.0000215091.77492.2a · Cited by 305 · see citing works
Aoki, T., Yamada, A., Aoyama, K., Saito, H., Tsuboi, A., Nakada, A., Niikura, R., Fujishiro, M., Oka, S., Ishihara, S., et al. (2018) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2018.10.027 · Cited by 300 · see citing works
Schoepfer, A. M., Beglinger, C., Straumann, A., Safroneeva, E., Romero, Y., Armstrong, D., Schmidt, C., Trummler, M., Pittet, V. E. H., & Vavricka, S. R. (2013) · Inflammatory Bowel Diseases · doi:10.1097/mib.0b013e3182810066 · Cited by 291 · see citing works
Stidham, R. W., Liu, W., Bishu, S., Rice, M. D., Higgins, P. D. R., Zhu, J., Nallamothu, B. K., & Waljee, A. K. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.3963 · Cited by 285 · see citing works
Takenaka, K., Ohtsuka, K., Fujii, T., Negi, M., Suzuki, K., Shimizu, H., Oshima, S., Akiyama, S., Motobayashi, M., Nagahori, M., et al. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.02.012 · Cited by 266 · see citing works
Maeda, Y., Kudo, S.-E., Mori, Y., Misawa, M., Ogata, N., Sasanuma, S., Wakamura, K., Oda, M., Mori, K., & Ohtsuka, K. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2018.09.024 · Cited by 245 · see citing works
Klang, E., Barash, Y., Margalit, R. Y., Soffer, S., Shimon, O., Albshesh, A., Ben‐Horin, S., Amitai, M. M., Eliakim, R., & Kopylov, U. (2019) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2019.11.012 · Cited by 234 · see citing works
Ozawa, T., Ishihara, S., Fujishiro, M., Saito, H., Kumagai, Y., Shichijo, S., Aoyama, K., & Tada, T. (2018) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2018.10.020 · Cited by 231 · see citing works
Tontini, G. E. (2015) · World Journal of Gastroenterology · doi:10.3748/wjg.v21.i1.21 · Cited by 220 · see citing works
A blood-based prognostic biomarker in IBD
Biasci, D., Lee, J. C., Noor, N. M., Pombal, D., Hou, M., Lewis, N. R., Ahmad, T., Hart, A. L., Parkes, M., McKinney, E. F., et al. (2019) · Gut · doi:10.1136/gutjnl-2019-318343 · Cited by 190 · see citing works
Wei, Z., Wang, W., Bradfield, J. P., Li, J., Cardinale, C. J., Frackelton, E. C., Kim, C., Mentch, F. D., Steen, K. V., Visscher, P. M., et al. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.05.002 · Cited by 184 · see citing works
The Role of Environmental Factors in the Pathogenesis of Inflammatory Bowel Diseases
Shouval, D. S., & Rufo, P. A. (2017) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2017.2571 · Cited by 175 · see citing works
Ananthakrishnan, A. N., Shi, H. Y., Tang, W., Law, C. C. Y., Sung, J. J. �., Chan, F. K. L., & Ng, S. C. (2016) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjw054 · Cited by 167 · see citing works
Classification of Paediatric Inflammatory Bowel Disease using Machine Learning
Mossotto, E., Ashton, J. J., Coelho, T. A. F., Beattie, R. M., MacArthur, B. D., & Ennis, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-02606-2 · Cited by 157 · see citing works
Disease Activity Assessment in IBD
Falvey, J. D., Hoskin, T. S., Meijer, B., Ashcroft, A., Walmsley, R. S., Day, A. S., & Gearry, R. B. (2015) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000000341 · Cited by 153 · see citing works
Central reading of ulcerative colitis clinical trial videos using neural networks
Gottlieb, K., Requa, J., Karnes, W., Chandra Gudivada, R., Shen, J., Rael, E., Arora, V., Dao, T., Ninh, A., & McGill, J. (2021) · Gastroenterology · doi:10.1053/j.gastro.2020.10.024 · Cited by 136 · see citing works
Histologic scoring indices for evaluation of disease activity in Crohn’s disease
Novak, G., Parker, C. E., Pai, R. K., MacDonald, J. K., Feagan, B. G., Sandborn, W. J., D’Haens, G., Jairath, V., & Khanna, R. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd012351.pub2 · Cited by 133 · see citing works
Artificial neural networks distinguish among subtypes of neoplastic colorectal lesions
Selaru, F. M., Xu, Y. H., Yin, J., Zou, T., Liu, T. C., Mori, Y., Abraham, J. M., Sato, F., Wang, S., Twigg, C., et al. (2002) · Gastroenterology · doi:10.1053/gast.2002.31904 · Cited by 133 · see citing works
Proteomic mass spectra classification using decision tree based ensemble methods
Geurts, P., Fillet, M., de Seny, D., Meuwis, M., Malaise, M., Merville, M.-P., & Wehenkel, L. A. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti494 · Cited by 131 · see citing works
Bossuyt, P. J., Nakase, H., Vermeire, S., Hertogh, G. D., Eelbode, T., Ferrante, M., Hasegawa, T., Willekens, H., Ikemoto, Y., Makino, T., et al. (2020) · Gut · doi:10.1136/gutjnl-2019-320056 · Cited by 125 · see citing works
Histologic Evaluation of Ulcerative Colitis
Mosli, M. H., Feagan, B. G., Sandborn, W. J., D’Haens, G., Behling, C. A., Kaplan, K. J., Driman, D. K., Shackelton, L. M., Baker, K. A., MacDonald, J. K., et al. (2014) · Inflammatory Bowel Diseases · doi:10.1097/01.mib.0000437986.00190.71 · Cited by 121 · see citing works
Multi-omics differentially classify disease state and treatment outcome in pediatric Crohn’s disease
Douglas, G. M., Hansen, R., Jones, C. M. A., Dunn, K. A., Comeau, A., Bielawski, J. P., Tayler, R., El-Omar, E. M., Russell, R. K., Hold, G. L., et al. (2018) · Microbiome · doi:10.1186/s40168-018-0398-3 · Cited by 119 · see citing works
Xie, T., Zhang, T., Ding, C., Dai, X., Li, Y., Guo, Z., Yao, W., Gong, J., Zhu, W., & Li, J. (2017) · Gastroenterology report · doi:10.1093/gastro/gox016 · Cited by 115 · see citing works
Genome-Wide Maps of Circulating miRNA Biomarkers for Ulcerative Colitis
Duttagupta, R., DiRienzo, S., Jiang, R., Bowers, J., Gollub, J., Kao, J., Kearney, K. R., Rudolph, D. S., Dawany, N. B., Showe, M. K., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0031241 · Cited by 113 · see citing works
Waljee, A. K., Lipson, R., Wiitala, W. L., Zhang, Y., Liu, B., Zhu, J., Wallace, B. I., Govani, S. M., Stidham, R. W., Hayward, R. A., et al. (2017) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izx007 · Cited by 113 · see citing works
Barash, Y., Azaria, L., Soffer, S., Yehuda, R. M., Shlomi, O., Ben‐Horin, S., Eliakim, R., Klang, E., & Kopylov, U. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.05.066 · Cited by 111 · see citing works
Romagnoni, A., Jégou, S., Steen, K. V., Wainrib, G., Hugot, J., Peyrin‐Biroulet, L., Chamaillard, M., Colombel, J.-F., Cottone, M., D’Amato, M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-46649-z · Cited by 100 · see citing works
Panaccione, R., Rutgeerts, P. J., Sandborn, W. J., Feagan, B. G., Schreiber, S., & Ghosh, S. K. (2008) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2008.03753.x · Cited by 99 · see citing works
Waljee, A. K., Wallace, B. I., Cohen‐Mekelburg, S., Liu, Y., Liu, B., Sauder, K. L., Stidham, R. W., Zhu, J., & Higgins, P. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.3721 · Cited by 99 · see citing works
Rubin, S. J. S., Bai, L., Haileselassie, Y., Garay, G., Yun, C., Becker, L. S., Streett, S. E., Sinha, S. R., & Habtezion, A. (2019) · Nature Communications · doi:10.1038/s41467-019-10387-7 · Cited by 98 · see citing works
Self-citationLuo, Y., Zhang, Y., Liu, M., Lai, Y., Liu, P., Wang, Z., Xing, T., Huang, Y., Li, Y., Li, A., et al. (2020) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-020-04802-4 · Cited by 98 · see citing works
Click, B. H., Rivers, C. R., Κoutroubakis, I. E., Babichenko, D., Anderson, A. J. M., Hashash, J. G., Dunn, M. A., Schwartz, M. B., Swoger, J. M., Baidoo, L., et al. (2016) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000000763 · Cited by 94 · see citing works
van der Sloot, K. W. J., Amini, M., Peters, V., Dijkstra, G. D. H., & Alizadeh, B. Z. (2017) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000001217 · Cited by 92 · see citing works
Emerging Therapies for Inflammatory Bowel Disease
Weisshof, R., Jurdi, K. E., Zmeter, N., & Rubin, D. T. (2018) · Advances in Therapy · doi:10.1007/s12325-018-0795-9 · Cited by 89 · see citing works
Assessment of Crohn’s Disease Lesions in Wireless Capsule Endoscopy Images
Kumar, R., Zhao, Q., Seshamani, S., Mullin, G. E., Hager, G. D., & Dassopoulos, T. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2172438 · Cited by 87 · see citing works
Machine Learning Algorithms for Objective Remission and Clinical Outcomes with Thiopurines
Waljee, A. K., Sauder, K. L., Patel, A. K., Segar, S., Liu, B., Zhang, Y., Zhu, J., Stidham, R. W., Balis, U. J., & Higgins, P. (2017) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjx014 · Cited by 85 · see citing works
Self-citationBielecki, C., Marquardt, C., Stallmach, A., Bocklitz, T. W., Schmitt, M. P., Popp, J., Krafft, C., Gharbi, A., & Knösel, T. (2012) · Journal of Biomedical Optics · doi:10.1117/1.jbo.17.7.076030 · Cited by 80 · see citing works
Waljee, A. K., Joyce, J. C., Wang, S., Saxena, A., Hart, M., Zhu, J., & Higgins, P. (2009) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2009.09.031 · Cited by 78 · see citing works
Predicting Corticosteroid-Free Biologic Remission with Vedolizumab in Crohn’s Disease
Waljee, A. K., Liu, B., Sauder, K. L., Zhu, J., Govani, S. M., Stidham, R. W., & Higgins, P. (2018) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izy031 · Cited by 68 · see citing works
Morilla, I., Uzzan, M., Laharie, D., Cazals-Hatem, D. L., Denost, Q., Bleiberg-Daniel, F., Belleannée, G., Bouhnik, Y., Wainrib, G., Panis, Y. H., et al. (2018) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2018.08.068 · Cited by 67 · see citing works
Isakov, O., Dotan, I., & Ben‐Shachar, S. (2017) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000001222 · Cited by 66 · see citing works
Deep learning enabled classification of Mayo endoscopic subscore in patients with ulcerative colitis
Bhambhvani, H. P., & Zamora, A. (2021) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000001952 · Cited by 59 · see citing works
Identifying Crohn’s disease signal from variome analysis
Wang, Y., Miller, M., Astrakhan, Y., Petersen, B., Schreiber, S., Franke, A., & Bromberg, Y. (2019) · Genome Medicine · doi:10.1186/s13073-019-0670-6 · Cited by 49 · see citing works
Wang, L., Fan, R., Zhang, C., Hong, L., Zhang, T., Chen, Y., Liu, K., Wang, Z., & Zhong, J. (2020) · Patient Preference and Adherence · doi:10.2147/ppa.s253732 · Cited by 49 · see citing works
Ungaro, R. C., Hu, L., Ji, J., Nayar, S., Kugathasan, S., Denson, L. A., Hyams, J. S., Dubinsky, M. C., Sands, B. E., & Cho, J. H. (2020) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.16136 · Cited by 48 · see citing works
Tong, Y., Lu, K., Yang, Y., Li, J., Lin, Y., Wu, D., Yang, A., Li, Y., Yu, S., & Qian, J. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01277-w · Cited by 46 · see citing works
Bettenworth, D., López, R., Hindryckx, P., Levesque, B. G., & Rieder, F. (2016) · Journal of Gastroenterology · doi:10.1007/s00535-016-1172-6 · Cited by 44 · see citing works
Reddy, B. K., Delen, D., & Agrawal, R. K. (2018) · Health Informatics Journal · doi:10.1177/1460458217751015 · Cited by 44 · see citing works
Sparse Modeling Reveals miRNA Signatures for Diagnostics of Inflammatory Bowel Disease
Hübenthal, M., Hemmrich‐Stanisak, G., Degenhardt, F. C., Szymczak, S., Du, Z. G., ElSharawy, A., Keller, A., Schreiber, S., & Franke, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140155 · Cited by 43 · see citing works
Daneshjou, R., Wang, Y., Bromberg, Y., Bovo, S., Martelli, P. L., Babbi, G., Lena, P. D., Casadio, R., Edwards, M. D., Gifford, D. K., et al. (2017) · Human Mutation · doi:10.1002/humu.23280 · Cited by 43 · see citing works
Smolander, J., Dehmer, M., & Emmert‐Streib, F. (2019) · FEBS Open Bio · doi:10.1002/2211-5463.12652 · Cited by 43 · see citing works
Charisis, V. S., & Hadjileontiadis, L. J. (2016) · World Journal of Gastroenterology · doi:10.3748/wjg.v22.i39.8641 · Cited by 41 · see citing works
Kang, T., Ding, W., Zhang, L., Ziemek, D., & Zarringhalam, K. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1984-2 · Cited by 40 · see citing works
Active learning based segmentation of Crohns disease from abdominal MRI
Mahapatra, D., Vos, F. M., & Buhmann, J. M. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.01.014 · Cited by 39 · see citing works
Lamash, Y., Kurugol, S., Freiman, M., Pérez-Rosselló, J. M., Callahan, M. J., Bousvaros, A., & Warfield, S. K. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26330 · Cited by 39 · see citing works
Alignment-free supervised classification of metagenomes by recursive SVM
Cui, H., & Zhang, X. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-641 · Cited by 38 · see citing works
Bossuyt, P. J., Hertogh, G. D., Eelbode, T., Vermeire, S., & Bisschops, R. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.09.053 · Cited by 37 · see citing works
Yuan, F., Zhang, Y., Kong, X., & Cai, Y. (2017) · BioMed Research International · doi:10.1155/2017/5741948 · Cited by 34 · see citing works
Li, H., Lai, L., & Shen, J. (2020) · Aging · doi:10.18632/aging.103861 · Cited by 34 · see citing works
Weersma, R. K., Xavier, R. J., Weersma, R. K., Barrett, J. C., Vermeire, S., Xavier, R. J., Anderson, C. A., Wijmenga, C., Daly, M. J., Alm, E. J., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.07.039 · Cited by 32 · see citing works
A novel surgical predictive model for Chinese Crohn's disease patients
Dong, Y., Xu, L., Fan, Y., Xiang, P., Gao, X., Chen, Y., Zhang, W., & Ge, Q. (2019) · Medicine · doi:10.1097/md.0000000000017510 · Cited by 30 · see citing works
Anderson, A. J. M., Click, B. H., Ramos–Rivers, C., Babichenko, D., Κoutroubakis, I. E., Hartman, D. J., Hashash, J. G., Schwartz, M. B., Swoger, J. M., Barrie, A. M., et al. (2016) · Digestive Diseases and Sciences · doi:10.1007/s10620-016-4278-z · Cited by 28 · see citing works
Han, L., Maciejewski, M., Brockel, C., Gordon, W., Snapper, S. B., Korzenik, J. R., Afzelius, L., & Altman, R. B. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx651 · Cited by 28 · see citing works
Biomarker discovery in inflammatory bowel diseases using network-based feature selection
Abbas, M. M., Matta, J., Le, T., Bensmail, H., Obafemi-Ajayi, T., Honavar, V., & EL‐Manzalawy, Y. (2019) · PLoS ONE · doi:10.1371/journal.pone.0225382 · Cited by 28 · see citing works
Diagnosis and misdiagnosis of inflammatory bowel disease
Papadakis, K. A., & TABIBZADEH, S. S. (2002) · Gastrointestinal Endoscopy Clinics of North America · doi:10.1016/s1052-5157(02)00005-3 · Cited by 27 · see citing works
Rozen, P., Lubin, F., Papo, N., Knaani, J., Farbstein, H., Farbstein, M., & Zajicek, G. (2001) · Cancer · doi:10.1002/1097-0142(20010215)91:4 · Cited by 27 · see citing works
Robust Microbial Markers for Non-Invasive Inflammatory Bowel Disease Identification
Wingfield, B., Coleman, S., McGinnity, T. M., & Bjourson, A. J. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2831212 · Cited by 25 · see citing works
Sofo, L., Caprino, P., Schena, C. A., Sacchetti, F., Potenza, A. E., & Ciociola, A. (2020) · PubMed · doi:10.26355/eurrev_202012_24178 · Cited by 22 · see citing works
Menti, E., Lanera, C., Lorenzoni, G., Giachino, D. F., Marchi, M., Gregori, D., & Berchialla, P. (2016) · PubMed Central · Cited by 18 · see citing works
Bottigliengo, D., Berchialla, P., Lanera, C., Azzolina, D., Lorenzoni, G., Martinato, M., Giachino, D. F., Baldi, I., & Gregori, D. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8060865 · Cited by 18 · see citing works
Ghoshal, U. C., Rai, S., Kulkarni, A., & Gupta, A. (2020) · JGH Open · doi:10.1002/jgh3.12342 · Cited by 18 · see citing works
Pal, L. R., Kundu, K., Yin, Y., & Moult, J. (2017) · Human Mutation · doi:10.1002/humu.23256 · Cited by 17 · see citing works
Popa, I. V., Burlacu, A., Mihai, C., & Prelipcean, C. C. (2020) · Medicina · doi:10.3390/medicina56110628 · Cited by 17 · see citing works
Syed, S., & Stidham, R. W. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa211 · Cited by 16 · see citing works
Objective Assessment of the Utility of Chromoendoscopy with a Support Vector Machine
Ogawa, R., Nishikawa, J., Hideura, E., Goto, A., Koto, Y., Ito, S., Unno, M., Yamaoka, Y., Kawasato, R., Hashimoto, S., et al. (2018) · Journal of Gastrointestinal Cancer · doi:10.1007/s12029-018-0083-6 · Cited by 11 · see citing works
Uttam, S., Hashash, J. G., LaFace, J., Binion, D. G., Regueiro, M. D., Hartman, D. J., Brand, R. E., & Liu, Y. (2019) · Cancer Prevention Research · doi:10.1158/1940-6207.capr-19-0024 · Cited by 11 · see citing works
Lerrigo, R., Coffey, J. T. R., Kravitz, J., Jadhav, P., Nikfarjam, A., Shah, N. H., Jurafsky, D., & Sinha, S. R. (2019) · Crohn s & Colitis 360 · doi:10.1093/crocol/otz011 · Cited by 10 · see citing works
Self-citationMachine learning for the quality of life in inflammatory bowel disease.
Babić, A., Šter, B., Pavesic, N., & Wigertz, O. B. (1997) · PubMed · Cited by 4 · see citing works
Marvi-Khorasani, H., Usefi, H., & Peña‐Castillo, L. (2020) · Scientific Reports · doi:10.1038/s41598-020-70583-0
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Bias in AI-based models for medical applications: challenges and mitigation strategies
Mittermaier, M., Raza, M. M., & Kvedar, J. C. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00858-z · Cited by 372 · see citing works
Bedrikovetski, S., Dudi-Venkata, N. N., Kroon, H. M., Seow, W., Vather, R., Carneiro, G., Moore, J. W., & Sammour, T. (2021) · BMC Cancer · doi:10.1186/s12885-021-08773-w · Cited by 164 · see citing works
Canine chronic enteropathy—Current state-of-the-art and emerging concepts
Jergens, A. E., & Heilmann, R. M. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.923013 · Cited by 116 · see citing works
Cross-scale multi-instance learning for pathological image diagnosis
Deng, R., Cui, C., Remedios, L. W., Bao, S., Womick, R. M., Chiron, S., Li, J., Roland, J. T., Lau, K. S., Liu, Q., et al. (2024) · Medical Image Analysis · doi:10.1016/j.media.2024.103124 · Cited by 95 · see citing works
Artificial Intelligence in Genetics
Vilhekar, R. S., & Rawekar, A. (2024) · Cureus · doi:10.7759/cureus.52035 · Cited by 98 · see citing works
“…has made it simpler to analyze, combine, and interpret huge genetics datasets, the requirement for objective prospective validation studies, as well as the substantial heterogeneity in AI methods, datasets, and clinical outcomes, are currently preventing the use of AI in clinical practice [34].”
BackgroundAnti-Integrins for the Treatment of Inflammatory Bowel Disease: Current Evidence and Perspectives
Gubatan, J. M. B., Keyashian, K., Rubin, S. J. S., Wang, J., Buckman, C., & Sinha, S. R. (2021) · Clinical and Experimental Gastroenterology · doi:10.2147/ceg.s293272 · Cited by 88 · see citing works
Self-citationMuzammil, M. A., Fariha, F., Patel, T., Sohail, R., Kumar, M., Khan, E. A., Khanam, B., Kumar, S., Khatri, M., Varrassi, G., et al. (2023) · Cureus · doi:10.7759/cureus.41120 · Cited by 76 · see citing works
“Large datasets from electronic health records, genomes, proteomics, and imaging modalities can be analyzed by AI applications like machine learning algorithms to get important insights into illness diagnosis, prognosis, and therapy response [67].”
BackgroundInflammation, Autoinflammation and Autoimmunity in Inflammatory Bowel Diseases
Padoan, A., Musso, G., Contran, N., & Basso, D. (2023) · Current Issues in Molecular Biology · doi:10.3390/cimb45070350 · Cited by 71 · see citing works
“Interestingly, colonoscopy and endoscopic images were used for generating AI model, with or without the addition of biochemical and molecular data and gene expression profiles [159], and very relevant results on sensitivity and specificity were found.”
BackgroundMethodInfluentialInflammatory bowel diseases: pathological mechanisms and therapeutic perspectives
Yang, X.-N., Guo, H., & Zou, M. (2026) · Molecular Biomedicine · doi:10.1186/s43556-025-00395-z · Cited by 75 · see citing works
“While systems biology and machine learning have introduced novel avenues for multi-omic integration, methodological heterogeneity and variable clinical endpoints continue to impede their translational application [582].”
Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review
Sedaño, R., Solitano, V., Vuyyuru, S. K., Yuan, Y., Hanžel, J., Ma, C., Nardone, O. M., & Jairath, V. (2025) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848251321915 · Cited by 57 · see citing works
Kraszewski, S., Szczurek, W., Szymczak, J. E., Reguła, M., & Neubauer, K. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10204745 · Cited by 47 · see citing works
Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials
Ahmad, H. A., East, J. E., Panaccione, R., Travis, S. P. L., Canavan, J. B., Usiskin, K., & Byrne, M. F. (2023) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjad029 · Cited by 47 · see citing works
Pinton, P. (2023) · Annals of Medicine · doi:10.1080/07853890.2023.2300670 · Cited by 42 · see citing works
“Most studies evaluating AI for the diagnosis of IBD are based on either ML or convolutional neural networks to analyze genetic/genomic datasets, imaging and endoscopy datasets, and protein expression/proteomics datasets [3].”
BackgroundInfluentialCè, M., D’Amico, N. C., Danesini, G. M., Foschini, C., Oliva, G., Martinenghi, C. M. A., & Cellina, M. I. (2023) · BioMedInformatics · doi:10.3390/biomedinformatics3010002 · Cited by 41 · see citing works
Precision Medicine in Inflammatory Bowel Disease: A Spotlight on Emerging Molecular Biomarkers
Meštrović, A., Perković, N., Božić, D., Kumrić, M., Vilović, M., & Božić, J. (2024) · Biomedicines · doi:10.3390/biomedicines12071520 · Cited by 36 · see citing works
“These studies should involve large, diverse patient cohorts to validate the predictive accuracy and clinical utility of AI models [187,188].”
BackgroundGomollón, F., Gisbert, J. P., Guerra, I., Plaza, R., Pajares, R., Almazán, L. M., Martín, M. C. L., Antonaya, M. D., Mendoza, M. I. V., Aparicio, J., et al. (2021) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000002317 · Cited by 35 · see citing works
Highlighting the Undetectable — Fluorescence Molecular Imaging in Gastrointestinal Endoscopy
Stibbe, J. A., Hoogland, P., Achterberg, F. B., Holman, D., Sojwal, R. S., Burggraaf, J., Vahrmeijer, A. L., Nagengast, W. B., & Rogalla, S. (2022) · Molecular Imaging and Biology · doi:10.1007/s11307-022-01741-1 · Cited by 34 · see citing works
New AI model for neoplasia detection and characterisation in inflammatory bowel disease
Abdelrahim, M., Siggens, K., Iwadate, Y., Maeda, N., Htet, H. M. T., & Bhandari, P. (2024) · Gut · doi:10.1136/gutjnl-2023-330718 · Cited by 34 · see citing works
“13 However, despite neoplasia detection being the primary aim of endoscopic surveillance in IBD, data on the application of this technology in neoplasia detection during endoscopic surveillance in an IBD setting remain very limited.”
BackgroundRole of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review
Majidova, K., Handfield, J., Kafi, K., Martin, R., & Kubinski, R. (2021) · Genes · doi:10.3390/genes12101465 · Cited by 34 · see citing works
Chen, L., Zhang, C., Niu, R., Xiong, S., He, J., Wang, Y., Zhang, P., Su, F., Liu, Z., Zhou, L., et al. (2024) · United European Gastroenterology Journal · doi:10.1002/ueg2.12720 · Cited by 37 · see citing works
“Promisingly, incorporating advanced technologies such as spatial transcriptomics, single‐ cell sequencing, systems biology, and artificial intelligence holds substantial potential for the in‐depth exploration of predictive biomarkers [91, 92].”
Balderramo, D. C. (2022) · World Journal of Gastroenterology · doi:10.3748/wjg.v28.i47.6743 · Cited by 33 · see citing works
Kiyokawa, H., Abe, M., Matsui, T., Kurashige, M., Ohshima, K., Tahara, S., Nojima, S., Ogino, T., Sekido, Y., Mizushima, T., et al. (2022) · The American Journal of Pathology · doi:10.1016/j.ajpath.2022.03.006 · Cited by 32 · see citing works
Stanković, B., Kotur, N., Nikčević, G., Gašić, V., Zukić, B., & Pavlović, S. (2021) · Genes · doi:10.3390/genes12091438 · Cited by 31 · see citing works
“One of the key challenges in this process is to effectively utilize information obtained in omics studies with patients’ data stored in electronic medical records (biochemistry tests, various imaging data, symptoms at diagnosis and lifestyle specifics) [38,91].”
BackgroundKang, S., Kim, H., Kim, S., Kim, J., Park, S., Lee, C.-W., Kim, K. O., Seo, G. S., Kim, M. S., Cha, J. M., et al. (2023) · Microorganisms · doi:10.3390/microorganisms11071665 · Cited by 29 · see citing works
Zhang, L., Mao, R., Lau, C. T., Chung, W. C., Chan, J. C. P., Liang, F., Zhao, C., Zhang, X., & Bian, Z. (2022) · Scientific Reports · doi:10.1038/s41598-022-14048-6 · Cited by 27 · see citing works
He, J., Wang, S.-X., & Liu, P. (2024) · British Journal of Radiology · doi:10.1093/bjr/tqae098 · Cited by 24 · see citing works
Computational models in inflammatory bowel disease
Pinton, P. (2022) · Clinical and Translational Science · doi:10.1111/cts.13228 · Cited by 24 · see citing works
A Review on Inflammatory Bowel Diseases: Recent Molecular Pathophysiology Advances
Abdulla, M., & Mohammed, N. (2022) · Biologics · doi:10.2147/btt.s380027 · Cited by 21 · see citing works
“In order to treat the massive data obtained, new algorithms and COVID –IBD data analysis methods should be elaborated by IA, ML and DL methods.(118)”
MethodLewis, J. R., Pathan, S., Kumar, P., & Dias, C. C. (2024) · IEEE Access · doi:10.1109/access.2024.3483432 · Cited by 21 · see citing works
Systematic review of artificial intelligence‐based image diagnosis for inflammatory bowel disease
Kawamoto, A., Takenaka, K., Okamoto, R., Watanabe, M., & Ohtsuka, K. (2022) · Digestive Endoscopy · doi:10.1111/den.14334 · Cited by 19 · see citing works
Vermeer, E., Jagt, J. Z., Lap, E. M., Struys, E. A., Budding, A. E., Verhoeven‐Duif, N. M., Bosma, M., Limbergen, J. V., Koot, B. G. P., de Jonge, R., et al. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2517828 · Cited by 18 · see citing works
Cai, W., Xu, J., Chen, Y., Wu, X. H., Zeng, Y., & Yu, F. (2023) · Inflammation · doi:10.1007/s10753-023-01827-0 · Cited by 17 · see citing works
“A variety of applications of ML in IBD is reviewed elsewhere [33].”
BackgroundMethodLu, J., Wang, Z., Maimaiti, M., Hui, W., Abudourexiti, A., & Gao, F. (2021) · Human Cell · doi:10.1007/s13577-021-00641-w · Cited by 17 · see citing works
“Machine learning has been evaluated for clinical applications in patients with inflammatory bowel disease [14].”
Deo, D. D., Buono, A. D., Gabbiadini, R., Nardone, O. M., Ferreiro‐Iglesias, R., Privitera, G., Bonifacio, C., Acosta, M. B.-D., Bezzio, C., & Armuzzi, A. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1637159 · Cited by 16 · see citing works
Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples
Afonso, C. L., & Afonso, A. M. (2023) · Veterinary Sciences · doi:10.3390/vetsci10120690 · Cited by 17 · see citing works
Artificial intelligence and inflammatory bowel disease: practicalities and future prospects
Brooks-Warburton, J., Ashton, J. J., Dhar, A., Tham, T., Allen, P. B., Hoque, S., Lovat, L. B., & Sebastian, S. (2021) · Frontline Gastroenterology · doi:10.1136/flgastro-2021-102003 · Cited by 16 · see citing works
“Most of the existing literature on the use of AI in diagnosing IBD relates to developing risk prediction models using either ML or CNNs to analyse endoscopic and imaging datasets (8).”
Yang, L., Perry, E., Shan, L. L., Wilding, H., Connell, W., Thompson, A. J. V., Taylor, A. C. F., Desmond, P. V., & Holt, B. A. (2022) · Endoscopy International Open · doi:10.1055/a-1846-0642 · Cited by 16 · see citing works
Røyset, E. S., Pettersen, H. S., Xu, W., Larbi, A., Sandvik, A. K., Steigen, S. E., Catalán‐Serra, I., & Bakke, I. (2022) · The Journal of Pathology Clinical Research · doi:10.1002/cjp2.301 · Cited by 15 · see citing works
Zhong, W., Gong, J., Su, Q., Farag, M. A., Simal‐Gándara, J., Wang, H., & Cao, H. (2023) · Phytochemistry Reviews · doi:10.1007/s11101-023-09866-z · Cited by 15 · see citing works
“Additionally, these technologies can assist researchers in conducting genomic and metabolomic studies to identify the molecular mechanisms of polyphenols and evaluate their effectiveness in different individuals (Gubatan et al. 2021).”
Aljohani, A. A., Alharbe, N., Mamlook, R. E. A., & Khayyat, M. M. (2024) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2024.101961 · Cited by 15 · see citing works
Li, X., Yan, L., Wang, X., Ouyang, C., Wang, C., Chao, J., Zhang, J., & Lian, G. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.1043412 · Cited by 13 · see citing works
Qiu, Y., Hu, S., Chao, K., Huang, L.-J., Huang, Z., Mao, R., Su, F., Zhang, C., Lin, X., Cao, Q., et al. (2024) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izae176 · Cited by 18 · see citing works
Akbarian, P., Asadi, F., & Sabahi, A. (2024) · Middle East Journal of Digestive Diseases · doi:10.34172/mejdd.2024.394 · Cited by 13 · see citing works
Boodaghidizaji, M., Cantu-Jungles, T. M., Chen, T., Zhang, B., Yao, T., LANDAY, A. L., Keshavarzian, A., Hamaker, B. R., & Ardekani, A. M. (2025) · BMC Microbiology · doi:10.1186/s12866-025-04072-7 · Cited by 12 · see citing works
“Both CNN and ANN have been applied to different classification problems involving gut microbiota, such as obesity [28], inflammatory bowel disease [29], and Parkinson’s disease [30] detection.”
Cardoso, P., Mascarenhas, M., Afonso, J. P. L., Ribeiro, T., Mendes, F., Martins, M., Andrade, P., Cardoso, H., Mascarenhas, M., Ferreira, J. P. S., et al. (2024) · Therapeutic Advances in Gastroenterology · doi:10.1177/17562848241251569 · Cited by 12 · see citing works
Yu, S., Zhang, M., Ye, Z., Wang, Y., Wang, X., & Chen, Y. (2023) · Cell Regeneration · doi:10.1186/s13619-022-00143-6 · Cited by 11 · see citing works
“Machine learning has facilitated the diagnosis and risk prediction of IBD, but there was considerable variability in the performance of the different algorithms across the various cohorts (Gubatan et al. 2021).”
BackgroundKhachatryan, L., Xiang, Y., Ivanov, A. B., Glaab, E., Graham, G. T., Granata, I., Giordano, M., Maddalena, L., Piccirillo, M., Manipur, I., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-33050-0 · Cited by 12 · see citing works
“There were several studies dedicated to microbiome-based IBD diagnostics prior to the MEDIC challenge; however, most of these works are at risk of bias since the same sample cohort is used for algorithm training and testing, introducing potential limitations with generalizability 12 .”
Sinha, T., Zain, Z., Bokhari, S. F. H., Waheed, S., Reza, T., Eze-odurukwe, A., Patel, M., Almadhoun, M. K. I. K., Hussain, A., & Reyaz, I. (2024) · Cureus · doi:10.7759/cureus.55268 · Cited by 11 · see citing works
“Machine learning and artificial intelligence can be employed for analyzing large datasets to identify new risk factors, predict disease progression, and personalize treatment strategies [71,72].”
BackgroundDiscontinuation of therapy in inflammatory bowel disease: Current views
Meštrović, A., Kumrić, M., & Božić, J. (2024) · World Journal of Clinical Cases · doi:10.12998/wjcc.v12.i10.1718 · Cited by 11 · see citing works
“In 2017, a consensus expert panel convened by the European Crohn’s and Colitis Organization gave practical instructions for exit strategies in the treatment of IBD[11].”
Deng, R., Cui, C., Remedios, L. W., Bao, S., Womick, R. M., Chiron, S., Li, J., Roland, J. T., Lau, K. S., Liu, Q., et al. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18814-5_3 · Cited by 11 · see citing works
MICDnet: Multimodal information processing networks for Crohn’s disease diagnosis
Jia, Z., Wang, Y., Li, S., Yang, M., Liu, Z., & Zhang, H. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107790 · Cited by 11 · see citing works
Chen, D. J., Fulmer, C. G., Gordon, I. O., Syed, S., Stidham, R. W., Casteele, N. V., Qin, Y., Falloon, K., Cohen, B. L., Wyllie, R., et al. (2021) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjab169 · Cited by 10 · see citing works
Spatz, S. J., & Afonso, C. L. (2024) · Veterinary Sciences · doi:10.3390/vetsci11060239 · Cited by 12 · see citing works
“Gubatan et al. (2021) reviewed existing artificial intelligence applications in inflammatory bowel disease [126].”
BackgroundZhou, Q., Lan, L., Wang, W., & Xu, X. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02853-8 · Cited by 10 · see citing works
Linking gene expression to clinical outcomes in pediatric Crohn’s disease using machine learning
Chen, K. A., Nishiyama, N. C., Ng, M. M. K., Shumway, A. J., Joisa, C. U., Schaner, M. R., Lian, G., Beasley, C., Zhu, L., Bantumilli, S., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-52678-0 · Cited by 10 · see citing works
“Our study has important limitations.”
Rubin, D. T., Gottlieb, K. T., Colombel, J., Schott, J.-P., Erisson, L., Prucka, B., Phillips, S. A., Kwon, J., Ng, J., & McGill, J. M. (2023) · Gastro Hep Advances · doi:10.1016/j.gastha.2023.06.003 · Cited by 9 · see citing works
Gene Identification in Inflammatory Bowel Disease via a Machine Learning Approach
Alfonso, G., & Castillo, R. (2023) · Medicina · doi:10.3390/medicina59071218 · Cited by 7 · see citing works
“This has been in part due to the large amount of data generated experimentally [41] and the need to come up with appropriate techniques to analyze such a large quantity of data.”
BackgroundSood, D., RIAZ, Z. M., Mikkilineni, J., Ravi, N. N., Chidipothu, V., Yerrapragada, G., Poonguzhali, E., Shariff, M. N., Natarajan, T. K., Janarthanan, J., et al. (2025) · Medical Sciences · doi:10.3390/medsci13040230 · Cited by 8 · see citing works
“No current system has been tested in real-world workflows or been shown to impact clinical endpoints [15,50,71,72].”
InfluentialBai, X., Guo, Y., Zhu, X.-M., & Dai, D. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002219 · Cited by 7 · see citing works
A review of disease risk prediction methods and applications in the omics era
Sun, C., Cheng, X., Xu, J., Chen, H., Tao, J., Dong, Y., Wei, S., Chen, R., Meng, X., Ma, Y., et al. (2024) · PROTEOMICS · doi:10.1002/pmic.202300359 · Cited by 7 · see citing works
Hassan, A., Abujabal, H. A. S., Ahmad, S., Malebary, S. J., & Alromema, N. A. (2024) · Diagnostics · doi:10.3390/diagnostics14111182 · Cited by 6 · see citing works
Gastrointestinal disorders in children with autism: Could artificial intelligence help?
Al‐Beltagi, M., Saeed, N. K., & Qaraghuli, S. (2022) · Artificial Intelligence in Gastroenterology · doi:10.35712/aig.v3.i1.1 · Cited by 6 · see citing works
Neklesova, M. V., Sogomonyan, K. S., Golovkin, I. A., Shirokiy, N. I., Vershinina, S. O., Tsvetikova, S. A., Korzhova, J. E., Zakharova, M. N., & Gnedovskaya, E. V. (2025) · Biomedicines · doi:10.3390/biomedicines13081806 · Cited by 6 · see citing works
“…significance of the microbiota in MS. Advances in machine learning (ML) have enabled the identification of disease-specific microbial signatures in conditions such as type 2 diabetes (T2D) [16], inflammatory bowel diseases (IBD) [17], colorectal cancer (CRC) [18], and neurological disorders [19].”
Jeong, J., Hong, S. T., Ullah, I., Kim, E. S., & Park, S. H. (2022) · Diagnostics · doi:10.3390/diagnostics12020288 · Cited by 5 · see citing works
“To the best of our knowledge, no study has classified CRC and IBD by using a machine learning-based method.”
MethodInfluentialGavrilescu, O., Popa, I. V., Dranga, M., Mihai, R., Prelipcean, C. C., & Mihai, C. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12113609 · Cited by 5 · see citing works
Horvath, A.-S., & Pouliou, P. (2024) · CRC Press eBooks · doi:10.1201/9781003425724-46 · Cited by 5 · see citing works
Cold, K. M., Vamadevan, A., Heen, A., Vilmann, A. S., Rasmussen, M., Konge, L., & Svendsen, M. B. S. (2025) · Diagnostics · doi:10.3390/diagnostics15050591 · Cited by 5 · see citing works
Ricci, L., Toussaint, Y., Becker, J., Najjar, H., Renier, A., Choukour, M., Buisson, A., Devos, C., Epstein, J., Peyrin‐Biroulet, L., et al. (2021) · Digestive and Liver Disease · doi:10.1016/j.dld.2021.09.005 · Cited by 5 · see citing works
Thick Data Analytics for Rating Ulcerative Colitis Severity Using Small Endoscopy Image Sample
Fiaidhi, J., Zezos, P., & Mohammed, S. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671327 · Cited by 5 · see citing works
“…learning has helped the analysis of large datasets in UC/CD conditions and in clustering the severity of these conditions reaching the level of human reviewer [3], significant heterogeneity exists in machine learning methods relevant to the available datasets with varying clinical outcomes [4].”
BackgroundArtificial intelligence in colorectal cancer screening in patients with inflammatory bowel disease
Marques, K. F., Marques, A. F., Lopes, M. A., Beraldo, R. F., Lima, T. B., & Sassaki, L. Y. (2022) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v3.i1.1 · Cited by 5 · see citing works
Liu, Z.-G., & Xie, S. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i42.112107 · Cited by 6 · see citing works
“Studies suggest that radiomics can provide objective measures of mucosal inflammation, potentially complementing or substituting for endoscopy in certain scenarios[23,24].”
Chai, E., Gu, X., Lu, Z., & Cui, T. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131298 · Cited by 4 · see citing works
Zeng, S., Dong, C., Liu, C., Zhen, J., Pu, Y., Hu, J., & Dong, W. (2025) · Digital Health · doi:10.1177/20552076251326217 · Cited by 4 · see citing works
Kumarasamy, G., Salim, N. H. M., Afandi, N. S. M., Habib, M. A. H., Amin, N. D. M., Ismail, M. N., & Musa, M. (2023) · Future Oncology · doi:10.2217/fon-2023-0704 · Cited by 4 · see citing works
Role of artificial intelligence in Crohn's disease intestinal strictures and fibrosis
Chen, Y. F., Liu, L., Lü, B., Yang, Y., Zheng, S. S., Huang, X., Xu, Y., & Fan, Y. H. (2024) · Journal of Digestive Diseases · doi:10.1111/1751-2980.13308 · Cited by 4 · see citing works
“15 In addition to its utility in endoscopic techniques, AI has also been reported to have a high accuracy in evaluating the severity of IBD based on clinical data of the patients, such as biochemical parameters, with algorithms such as CNN and SVM. 16 Studies have employed ML algorithms to predict disease risk based o…”
InfluentialGuo, L., Fu, K., Wang, W., Zhou, L. Q., Chen, L., & Jiang, M. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01878-9 · Cited by 3 · see citing works
“AI has been employed for assessing colonic polyps [24], adenomas [25], colorectal cancer [26], ulcerative colitis [27], and intestinal motility disorders in colorectal diseases [28].”
Palmieri, O., Cannarozzi, A. L., Latiano, A., Massimino, L., Bossa, F., Riva, M., Ungaro, F., Guerra, M., Brina, A. L. P. D., Carparelli, S., et al. (2025) · Gut Pathogens · doi:10.1186/s13099-025-00758-5 · Cited by 3 · see citing works
“Among the applications of AI in IBD, the integration and analysis of multi-omics data have enabled a better understanding of disease pathogenesis and the identification of diagnostic and therapeutic biomarkers [30].”
Suarez, M., Martínez, R., González-Martínez, F., Torres, A. M., & Mateo, J. (2026) · World Journal of Hepatology · doi:10.4254/wjh.v18.i2.114834 · Cited by 3 · see citing works
Huynh, C. D. (2026) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2026.100386 · Cited by 4 · see citing works
Woźniak, S., Pawluś, A., Grzelak, J., Chobotow, S., Paulsen, F. P., Olchowy, C., & Zaleska–Dorobisz, U. (2022) · Anatomical Science International · doi:10.1007/s12565-022-00681-8 · Cited by 3 · see citing works
“It can also be used to assess the quality of bowel preparation before the examination (Gubatan et al. 2021; Yang and Bang 2019; Yoo et al. 2021).”
MethodManzari, Z. S., Ghaderi, M. S., Vossoughinia, H., Rafiei, H., & Mafi, M. H. (2024) · Saudi Journal of Gastroenterology · doi:10.4103/sjg.sjg_200_24 · Cited by 3 · see citing works
Central resources of variant discovery and annotation and its role in precision medicine
Halim‐Fikri, H., Syed-Hassan, S.-N. R.-K., Juhari, W. K. W., Assyuhada, M. G. S. N., Hernaningsih, Y., Yusoff, N. M., Merican, A. F., & Zilfalil, B. A. (2022) · Asian Biomedicine · doi:10.2478/abm-2022-0032 · Cited by 3 · see citing works
“To illustrate, Gubatan et al. [81] explained the most recent advanced technologies of AI in disease diagnosis, risk prediction, and disease severity evaluation, as well as prediction of clinical decision-making in IBD patients.”
BackgroundPredictive approaches in inflammatory bowel disease
Pinton, P. (2021) · Clinical and Translational Science · doi:10.1111/cts.13179 · Cited by 3 · see citing works
Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort
Marzullo, A., Savevski, V., Menini, M., Schilirò, A., Franchellucci, G., Dal Buono, A., Bezzio, C., Gabbiadini, R., Hassan, C., Repici, A., et al. (2025) · Data · doi:10.3390/data10070100 · Cited by 2 · see citing works
Biomarkers for Disease Activity in Inflammatory Bowel Disease
Devi, J., Samaan, S., Syal, G., & Deepak, P. (2025) · Gastroenterology Clinics of North America · doi:10.1016/j.gtc.2025.08.003 · Cited by 2 · see citing works
Chougule, P. R., & Sinha, S. N. (2025) · NAM journal. · doi:10.1016/j.namjnl.2025.100051 · Cited by 2 · see citing works
“The application of AI and ML in IBD has expanded rapidly, providing powerful in silico tools to analyse and interpret complex datasets, ranging from genetic and molecular signatures to histological images, endoscopic data, and electronic health records (Gubatan et al., 2021).”
Self-citationDignass, A. U., Narula, N., Pratap, K., Danese, S., Dotan, I., Iacucci, M., Jairath, V., Loftus, E. V., Magro, F., Mantzaris, G. J., et al. (2026) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjag007 · Cited by 2 · see citing works
najih, N. E., & Moussa, P. A. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-47366-1_1 · Cited by 1 · see citing works
Artificial intelligence and colonoscopy − enhancements and improvements
Yoo, B. S., D’Souza, S. M., Houston, K. V., Patel, A., Lau, J., Elmahdi, A., Parekh, P. J., & Johnson, D. (2021) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v2.i4.157 · Cited by 2 · see citing works
Self-citationDeSantis, A., Buss, K., Coker, K., Pasternak, B. A., Chi, J., Patterson, J. S., Gu, H., Jurutka, P. W., & Sandrin, T. R. (2025) · Biomolecules · doi:10.3390/biom15050746 · Cited by 1 · see citing works
“Leveraging machine learning and AI may also enhance predictive modeling for PIBD diagnosis and biomarker discovery [84].”
Gottlieb, K. T., Battioui, C., Wang, Y., Brodskiy, P., Haft‐Javaherian, M., Colucci, D. R., Thomason, D., & Baxi, S. S. (2025) · BMJ Open Gastroenterology · doi:10.1136/bmjgast-2025-001959 · Cited by 1 · see citing works
“6 7 16–18 While these endeavours have proven worthy, with several AI models exhibiting high agreement with human readers, relying solely on these models for central reading poses its own set of limitations, including a lack of regulatory guidance, unclear acceptance criteria and insufficient…”
Priorities for advancing artificial intelligence in inflammatory bowel disease: a technology update
Silverman, A. L., Stidham, R. W., Parasa, S., Sharma, P., & Mahadevan, U. (2026) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2025.12.248 · Cited by 1 · see citing works
Almajdi, A., & Shehab, M. (2026) · Biomedicines · doi:10.3390/biomedicines14040798 · Cited by 1 · see citing works
“Collectively, AI serves as a critical tool for personalized IBD care, supporting treat-ment decisions that reflect each patient’s unique biological, clinical, and molecular profile [38].”
Jiang, X., Li, P., Chen, Z., Yao, H., Jia, H., Wang, T., & Tang, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1841261 · Cited by 1 · see citing works
Artificial intelligence in gastrointestinal endoscopy: evolution to a new era
Ortíz, O., Esparrach, M. G. F., Daca-Álvarez, M., & Pellisé, M. (2022) · Revista Española de Enfermedades Digestivas · doi:10.17235/reed.2022.8961/2022 · Cited by 1 · see citing works
Afonso, J. P. L., Martins, M., Ferreira, J. P. S., & Mascarenhas, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99647-1.00003-4 · Cited by 2 · see citing works
Karakasidis, E. (2025) · Health and Technology · doi:10.1007/s12553-025-00997-1 · Cited by 1 · see citing works
Utility of artificial intelligence in colonoscopy
Shah, N., Jyala, A., Patel, H., & Makker, J. S. (2021) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v2.i3.79
Utility of artificial intelligence in colonoscopy
Shah, N., Jyala, A., Patel, H., & Makker, J. S. (2021) · Artificial Intelligence in Gastrointestinal Endoscopy · doi:10.37126/aige.v2.i3.78
Deep Learning Framework for Cancer Diagnosis and Treatment
Bahadur, S., & Kumar, P. (2022) · doi:10.1002/9781119857983.ch8
- Authors
- Gubatan, J., Levitte, S., Patel, A., Balabanis, T., Wei, M. T., & Sinha, S. R.
- Affiliations
- Stanford Medicine (US)
- Venue
- World Journal of Gastroenterology
- Publisher
- Baishideng Publishing Group Inc.
- Published
- 2021-05-07
- Volume
- 27
- Issue
- 17
- Pages
- 1920-1935
- ISSN
- 1007-9327 (ISSN-L)
- PMC
- PMC8108036
- OpenAlex
- W3157079437
- Cited by
- 121
- References
- 94
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 21 · 2023: 21 · 2022: 20 · 2021: 16
- FWCI
- 2.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- World Journal of Gastroenterology (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Inflammatory Bowel Disease
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- inflammatory bowel disease, artificial intelligence, machine learning, diagnosis, risk prediction, disease severity assessment, clinical outcome prediction, multi-omics data, endoscopy, endomicroscopy, electronic health records, clinical validation
- MeSH terms
- Artificial Intelligence, Colitis, Humans, Prospective Studies, Inflammatory Bowel Diseases, Metagenomics
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